The Role
Leads the architecture, development, and productionization of AI/ML and GenAI solutions for healthcare operations. Responsibilities include building clinical and claims data pipelines, developing LLM and RAG applications, implementing MLOps and LLMOps, ensuring HIPAA-compliant governance, translating business needs into technical roadmaps, communicating with stakeholders, and mentoring engineers.
Summary Generated by Built In
Requisition Number: 2391434
We are seeking a highly experienced Lead AI/ML Engineer to lead the discovery, design, and adoption of advanced optimisation and AI/ML solutions across mathematical programming, quantum-inspired methods, hybrid ML + optimisation, and Generative AI domains.
This role serves as a senior technical leader responsible for driving optimisation innovation, solving complex healthcare business problems, defining scalable solution strategies, and accelerating the transition of optimisation solutions from experimentation to production.
Owns optimisation strategy, architecture decisions, enterprise standards, reusable frameworks, capability development, and leadership of small teams while remaining deeply hands-on in solving critical business challenges.
Primary Responsibilities
Optimisation Strategy & Technical Leadership• Drive optimisation and AI solution strategy for complex, high-impact healthcare business problems.• Lead technical design, solution architecture, and optimisation technology selection decisions.• Establish reusable optimisation patterns, solver frameworks, standards, and best practices across the organisation.• Provide technical leadership and mentorship to AI/ML Engineers and cross-functional teams.• Evaluate emerging optimisation, quantum, and AI technologies and recommend enterprise adoption approaches.• Influence enterprise AI and optimisation strategy, architecture standards, and capability development.
Optimisation & AI/ML Modelling• Define the modelling strategy for complex business problems, setting the approach for mathematical optimisation and AI/ML solution design across the enterprise.• Set strategic direction for advanced AI/ML and optimisation model development across predictive, prescriptive, deep learning, and GenAI systems.• Own the formulation strategy for complex optimisation problems, including linear and non-linear programming, integer and combinatorial optimisation, and stochastic and robust optimisation.• Lead the development of new modelling paradigms combining ML and optimisation, including decision-focused learning, reinforcement learning, and constrained optimisation.• Drive the enterprise strategy for GenAI and optimisation integration, including retrieval optimisation, prompt optimisation, and constrained generation frameworks.• Architect scalable modelling frameworks that integrate optimisation solvers with ML/AI systems for enterprise-wide deployment.• Champion quantum and quantum-inspired optimisation methods, including QAOA, annealing approaches, and hybrid quantum-classical algorithms.
Applied Solution Development• Design and develop POCs, prototypes, and reference implementations for optimisation-driven use cases.• Build reusable assets including solver configurations, optimisation workflows, evaluation frameworks, and implementation accelerators.• Define production-ready solution blueprints to support engineering adoption of optimisation solutions.• Lead end-to-end lifecycle activities including problem formulation, modelling, solver selection, validation, deployment, monitoring, and continuous improvement.
Production Readiness & MLOps• Drive successful transition of validated optimisation solutions into production by partnering with engineering teams to ensure scalability, maintainability, and security.• Apply MLOps best practices including experiment tracking, solver versioning, CI/CD integration, performance monitoring, and observability.• Ensure operational readiness, model governance, and alignment with enterprise architecture standards.• Develop implementation-ready artefacts including reusable code, optimisation pipelines, solver integration patterns, and technical documentation.
Research & Innovation• Define the research agenda in optimisation, operations research, and quantum computing, directing investigation into high-impact healthcare applications.• Lead evaluation and enterprise adoption decisions for emerging AI and optimisation frameworks and technology stacks.• Lead and sponsor publication of research artefacts including white papers, patents, and internal frameworks.• Drive adoption of optimisation accelerators, reusable frameworks, and best practices across teams.
Responsible AI & Compliance• Establish evaluation, guardrail, and governance frameworks for optimisation and AI solutions.• Ensure explainability of optimisation decisions, fairness constraints, and regulatory alignment with HIPAA/PHI, SOC 2, and HITRUST.• Apply responsible AI principles, bias mitigation, and AI governance frameworks throughout the solution lifecycle.
Team & Organisational Impact• Lead a small team of AI/ML Engineers while remaining deeply hands-on in optimisation and AI solution development.• Mentor team members on optimisation methodologies, mathematical modelling, experimentation practices, and technical excellence.• Promote knowledge sharing, innovation, and adoption of reusable optimisation and AI capabilities.• Collaborate with business, product, architecture, and engineering teams to align solutions with measurable business outcomes.• Communicate solution results, trade-offs, and business impact to technical and non-technical stakeholders.
Stakeholder Engagement & Leadership• Accelerate organisational adoption of optimisation and AI by establishing repeatable patterns, reusable frameworks, and governance standards that reduce time-to-production.• Influence enterprise AI and optimisation strategy through thought leadership, stakeholder engagement, and cross-functional collaboration.• Communicate research findings, solution performance, strategic trade-offs, and business impact clearly to executive and non-technical stakeholders.• Lead and own technical solution design discussions, providing authoritative AI and optimisation architecture recommendations that balance business objectives, solver performance, scalability, and compliance requirements.• Drive strategic alignment between optimisation and AI solutions and business objectives across business, product, architecture, and engineering teams.
Measuring Success• Quality and business impact of optimisation, ML, and GenAI solutions.• Production readiness and successful deployment of validated solutions.• Percentage of POCs successfully adopted into production.• Adoption of reusable optimisation accelerators, frameworks, and reference architectures.• Reduction in experimentation-to-production cycle time.• Team capability growth and delivery of measurable business outcomes.
Comply with the terms and conditions of the employment contract, company policies and procedures, and any directives which may arise due to evolving business requirements. #njp #nic
Required Qualifications
* Bachelor's degree in Computer Science, Mathematics, Engineering, Operations Research, Applied Mathematics, or related field; Master's degree preferred.
* 9+ years of experience in applied AI/ML or optimisation-focused roles, with demonstrated leadership of enterprise-scale AI and optimisation initiatives.
* Proven experience leading complex optimisation and AI initiatives from ideation through production deployment.
* Strong foundation in mathematical optimisation, operations research, and statistics.
* Strong expertise in machine learning, deep learning, statistical modelling, predictive analytics, and experimentation.
* Hands-on experience with optimisation frameworks, including Pyomo, OR-Tools, Gurobi, and/or CPLEX.
* Hands-on experience with ML/DL frameworks: PyTorch and/or TensorFlow.
* Hands-on experience with Generative AI technologies including LLMs, RAG, prompt engineering, and optimisation-integrated generation frameworks.
* Experience developing Agentic AI solutions using orchestration frameworks and tool-enabled workflows.
* Demonstrated experience with combinatorial optimisation and large-scale decision systems.
* Proven experience with hybrid ML and optimisation approaches.
* Familiarity with quantum computing concepts or quantum-inspired algorithms for optimisation.
* Strong programming skills in Python, NumPy, pandas, scientific computing, and SQL.
* Strong knowledge of MLOps, model governance, and production AI and optimisation systems.
* Strong communication, stakeholder management, and technical leadership skills.
* Experience mentoring scientists and leading small technical teams.
Preferred Qualifications
* PhD or advanced degree in AI, ML, Computer Science, Mathematics, Statistics, Operations Research, or related discipline.
* Experience with quantum frameworks such as Qiskit, Cirq, D-Wave, or Azure Quantum.
* Experience with reinforcement learning for decision optimisation.
* Experience with simulation-based optimisation and digital twins.
* Healthcare domain expertise including claims, EHR/HL7/FHIR, care pathways, resource planning, ICD/CPT coding, risk adjustment, quality measures, and de-identification.
* Publications or patents in optimisation, ML, or quantum computing; contributions to internal frameworks, accelerators, or enterprise AI innovation initiatives.
* Experience integrating optimisation into production systems in collaboration with engineering teams.
* Experience establishing Responsible AI, governance, risk management, and compliance frameworks for constrained optimisation and decision-making systems.
* Experience building enterprise-scale Generative AI solutions integrated with optimisation pipelines.
* Big data platforms including Databricks, Snowflake, BigQuery, and Kafka; lakehouse patterns.
* MLOps stack including MLflow, SageMaker, Azure ML, or Vertex AI; model monitoring, observability, automated retraining, and deployment automation.
* Knowledge of security and compliance frameworks including SOC 2, HITRUST, and HIPAA.
* Experience designing enterprise AI platforms, optimisation solver services, and reusable ML frameworks.
* Experience mentoring teams and driving AI and optimisation capability development across organisations.
Technical Skills
* Optimisation: Mathematical Optimisation, Linear/Non-linear Programming, Integer & CombinatorialOptimisation, Stochastic Optimisation, Robust Optimisation, Operations Research, Large-Scale Decision Systems
* Optimisation Frameworks: Pyomo, OR-Tools, Gurobi, CPLEX
* Quantum & Quantum-Inspired: Quantum Optimisation, QAOA, Quantum Annealing, Hybrid Quantum-Classical Algorithms, Qiskit, Cirq, D-Wave, Azure Quantum
* AI/ML & Analytics: Machine Learning, Deep Learning, Statistical Modelling, Predictive Analytics, Prescriptive Analytics, Experimental Design
* Hybrid ML + Optimisation: Decision-Focused Learning, Reinforcement Learning, Constrained Optimisation, Simulation-Based Optimisation, Digital Twins
* Generative & Agentic AI: Generative AI, LLMs, RAG, Prompt Engineering, Retrieval Optimisation, Constrained Generation, Agentic AI, Agentic Workflows, Orchestration Frameworks, Tool Integration
* Programming & Data Engineering: Python, SQL, Feature Engineering, Data Pipelines, Model Evaluation, Experimentation Frameworks
* MLOps & Model Lifecycle: MLOps, MLflow, Kubeflow, CI/CD, Model Monitoring, Observability, Drift Detection, Model Registry, Deployment Automation
* Data Platforms: Databricks, Snowflake, BigQuery, Kafka, Lakehouse Architectures
* Responsible AI & Governance: Responsible AI, Explainability, Fairness Constraints, AI Governance, Risk Management, SOC 2, HITRUST, HIPAA
* Healthcare Analytics: Healthcare Analytics, Claims, Clinical Data, EHR/HL7/FHIR, ICD/CPT, Risk Adjustment, Population Health, Care Management
* Leadership & Strategy: Technical Leadership, AI Strategy, Optimisation Strategy, Innovation, Stakeholder Management, Mentoring, Team Leadership, Cross-Functional Collaboration
Careers with Optum. Here's the idea. We built an entire organization around one giant objective; make the health system work better for everyone. So when it comes to how we use the world's large accumulation of health-related information, or guide health and lifestyle choices or manage pharmacy benefits for millions, our first goal is to leap beyond the status quo and uncover new ways to serve. Optum, part of the UnitedHealth Group family of businesses, brings together some of the greatest minds and most advanced ideas on where health care has to go in order to reach its fullest potential. For you, that means working on high performance teams against sophisticated challenges that matter. Optum, incredible ideas in one incredible company and a singular opportunity to do your life's best work. SM
Diversity creates a healthier atmosphere: UnitedHealth Group is an Equal Employment Opportunity/Affirmative Action employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, protected veteran status, disability status, sexual orientation, gender identity or expression, marital status, genetic information, or any other characteristic protected by law.
UnitedHealth Group is a drug-free workplace. Candidates are required to pass a drug test before beginning employment.
We are seeking a highly experienced Lead AI/ML Engineer to lead the discovery, design, and adoption of advanced optimisation and AI/ML solutions across mathematical programming, quantum-inspired methods, hybrid ML + optimisation, and Generative AI domains.
This role serves as a senior technical leader responsible for driving optimisation innovation, solving complex healthcare business problems, defining scalable solution strategies, and accelerating the transition of optimisation solutions from experimentation to production.
Owns optimisation strategy, architecture decisions, enterprise standards, reusable frameworks, capability development, and leadership of small teams while remaining deeply hands-on in solving critical business challenges.
Primary Responsibilities
Optimisation Strategy & Technical Leadership• Drive optimisation and AI solution strategy for complex, high-impact healthcare business problems.• Lead technical design, solution architecture, and optimisation technology selection decisions.• Establish reusable optimisation patterns, solver frameworks, standards, and best practices across the organisation.• Provide technical leadership and mentorship to AI/ML Engineers and cross-functional teams.• Evaluate emerging optimisation, quantum, and AI technologies and recommend enterprise adoption approaches.• Influence enterprise AI and optimisation strategy, architecture standards, and capability development.
Optimisation & AI/ML Modelling• Define the modelling strategy for complex business problems, setting the approach for mathematical optimisation and AI/ML solution design across the enterprise.• Set strategic direction for advanced AI/ML and optimisation model development across predictive, prescriptive, deep learning, and GenAI systems.• Own the formulation strategy for complex optimisation problems, including linear and non-linear programming, integer and combinatorial optimisation, and stochastic and robust optimisation.• Lead the development of new modelling paradigms combining ML and optimisation, including decision-focused learning, reinforcement learning, and constrained optimisation.• Drive the enterprise strategy for GenAI and optimisation integration, including retrieval optimisation, prompt optimisation, and constrained generation frameworks.• Architect scalable modelling frameworks that integrate optimisation solvers with ML/AI systems for enterprise-wide deployment.• Champion quantum and quantum-inspired optimisation methods, including QAOA, annealing approaches, and hybrid quantum-classical algorithms.
Applied Solution Development• Design and develop POCs, prototypes, and reference implementations for optimisation-driven use cases.• Build reusable assets including solver configurations, optimisation workflows, evaluation frameworks, and implementation accelerators.• Define production-ready solution blueprints to support engineering adoption of optimisation solutions.• Lead end-to-end lifecycle activities including problem formulation, modelling, solver selection, validation, deployment, monitoring, and continuous improvement.
Production Readiness & MLOps• Drive successful transition of validated optimisation solutions into production by partnering with engineering teams to ensure scalability, maintainability, and security.• Apply MLOps best practices including experiment tracking, solver versioning, CI/CD integration, performance monitoring, and observability.• Ensure operational readiness, model governance, and alignment with enterprise architecture standards.• Develop implementation-ready artefacts including reusable code, optimisation pipelines, solver integration patterns, and technical documentation.
Research & Innovation• Define the research agenda in optimisation, operations research, and quantum computing, directing investigation into high-impact healthcare applications.• Lead evaluation and enterprise adoption decisions for emerging AI and optimisation frameworks and technology stacks.• Lead and sponsor publication of research artefacts including white papers, patents, and internal frameworks.• Drive adoption of optimisation accelerators, reusable frameworks, and best practices across teams.
Responsible AI & Compliance• Establish evaluation, guardrail, and governance frameworks for optimisation and AI solutions.• Ensure explainability of optimisation decisions, fairness constraints, and regulatory alignment with HIPAA/PHI, SOC 2, and HITRUST.• Apply responsible AI principles, bias mitigation, and AI governance frameworks throughout the solution lifecycle.
Team & Organisational Impact• Lead a small team of AI/ML Engineers while remaining deeply hands-on in optimisation and AI solution development.• Mentor team members on optimisation methodologies, mathematical modelling, experimentation practices, and technical excellence.• Promote knowledge sharing, innovation, and adoption of reusable optimisation and AI capabilities.• Collaborate with business, product, architecture, and engineering teams to align solutions with measurable business outcomes.• Communicate solution results, trade-offs, and business impact to technical and non-technical stakeholders.
Stakeholder Engagement & Leadership• Accelerate organisational adoption of optimisation and AI by establishing repeatable patterns, reusable frameworks, and governance standards that reduce time-to-production.• Influence enterprise AI and optimisation strategy through thought leadership, stakeholder engagement, and cross-functional collaboration.• Communicate research findings, solution performance, strategic trade-offs, and business impact clearly to executive and non-technical stakeholders.• Lead and own technical solution design discussions, providing authoritative AI and optimisation architecture recommendations that balance business objectives, solver performance, scalability, and compliance requirements.• Drive strategic alignment between optimisation and AI solutions and business objectives across business, product, architecture, and engineering teams.
Measuring Success• Quality and business impact of optimisation, ML, and GenAI solutions.• Production readiness and successful deployment of validated solutions.• Percentage of POCs successfully adopted into production.• Adoption of reusable optimisation accelerators, frameworks, and reference architectures.• Reduction in experimentation-to-production cycle time.• Team capability growth and delivery of measurable business outcomes.
Comply with the terms and conditions of the employment contract, company policies and procedures, and any directives which may arise due to evolving business requirements. #njp #nic
Required Qualifications
* Bachelor's degree in Computer Science, Mathematics, Engineering, Operations Research, Applied Mathematics, or related field; Master's degree preferred.
* 9+ years of experience in applied AI/ML or optimisation-focused roles, with demonstrated leadership of enterprise-scale AI and optimisation initiatives.
* Proven experience leading complex optimisation and AI initiatives from ideation through production deployment.
* Strong foundation in mathematical optimisation, operations research, and statistics.
* Strong expertise in machine learning, deep learning, statistical modelling, predictive analytics, and experimentation.
* Hands-on experience with optimisation frameworks, including Pyomo, OR-Tools, Gurobi, and/or CPLEX.
* Hands-on experience with ML/DL frameworks: PyTorch and/or TensorFlow.
* Hands-on experience with Generative AI technologies including LLMs, RAG, prompt engineering, and optimisation-integrated generation frameworks.
* Experience developing Agentic AI solutions using orchestration frameworks and tool-enabled workflows.
* Demonstrated experience with combinatorial optimisation and large-scale decision systems.
* Proven experience with hybrid ML and optimisation approaches.
* Familiarity with quantum computing concepts or quantum-inspired algorithms for optimisation.
* Strong programming skills in Python, NumPy, pandas, scientific computing, and SQL.
* Strong knowledge of MLOps, model governance, and production AI and optimisation systems.
* Strong communication, stakeholder management, and technical leadership skills.
* Experience mentoring scientists and leading small technical teams.
Preferred Qualifications
* PhD or advanced degree in AI, ML, Computer Science, Mathematics, Statistics, Operations Research, or related discipline.
* Experience with quantum frameworks such as Qiskit, Cirq, D-Wave, or Azure Quantum.
* Experience with reinforcement learning for decision optimisation.
* Experience with simulation-based optimisation and digital twins.
* Healthcare domain expertise including claims, EHR/HL7/FHIR, care pathways, resource planning, ICD/CPT coding, risk adjustment, quality measures, and de-identification.
* Publications or patents in optimisation, ML, or quantum computing; contributions to internal frameworks, accelerators, or enterprise AI innovation initiatives.
* Experience integrating optimisation into production systems in collaboration with engineering teams.
* Experience establishing Responsible AI, governance, risk management, and compliance frameworks for constrained optimisation and decision-making systems.
* Experience building enterprise-scale Generative AI solutions integrated with optimisation pipelines.
* Big data platforms including Databricks, Snowflake, BigQuery, and Kafka; lakehouse patterns.
* MLOps stack including MLflow, SageMaker, Azure ML, or Vertex AI; model monitoring, observability, automated retraining, and deployment automation.
* Knowledge of security and compliance frameworks including SOC 2, HITRUST, and HIPAA.
* Experience designing enterprise AI platforms, optimisation solver services, and reusable ML frameworks.
* Experience mentoring teams and driving AI and optimisation capability development across organisations.
Technical Skills
* Optimisation: Mathematical Optimisation, Linear/Non-linear Programming, Integer & CombinatorialOptimisation, Stochastic Optimisation, Robust Optimisation, Operations Research, Large-Scale Decision Systems
* Optimisation Frameworks: Pyomo, OR-Tools, Gurobi, CPLEX
* Quantum & Quantum-Inspired: Quantum Optimisation, QAOA, Quantum Annealing, Hybrid Quantum-Classical Algorithms, Qiskit, Cirq, D-Wave, Azure Quantum
* AI/ML & Analytics: Machine Learning, Deep Learning, Statistical Modelling, Predictive Analytics, Prescriptive Analytics, Experimental Design
* Hybrid ML + Optimisation: Decision-Focused Learning, Reinforcement Learning, Constrained Optimisation, Simulation-Based Optimisation, Digital Twins
* Generative & Agentic AI: Generative AI, LLMs, RAG, Prompt Engineering, Retrieval Optimisation, Constrained Generation, Agentic AI, Agentic Workflows, Orchestration Frameworks, Tool Integration
* Programming & Data Engineering: Python, SQL, Feature Engineering, Data Pipelines, Model Evaluation, Experimentation Frameworks
* MLOps & Model Lifecycle: MLOps, MLflow, Kubeflow, CI/CD, Model Monitoring, Observability, Drift Detection, Model Registry, Deployment Automation
* Data Platforms: Databricks, Snowflake, BigQuery, Kafka, Lakehouse Architectures
* Responsible AI & Governance: Responsible AI, Explainability, Fairness Constraints, AI Governance, Risk Management, SOC 2, HITRUST, HIPAA
* Healthcare Analytics: Healthcare Analytics, Claims, Clinical Data, EHR/HL7/FHIR, ICD/CPT, Risk Adjustment, Population Health, Care Management
* Leadership & Strategy: Technical Leadership, AI Strategy, Optimisation Strategy, Innovation, Stakeholder Management, Mentoring, Team Leadership, Cross-Functional Collaboration
Careers with Optum. Here's the idea. We built an entire organization around one giant objective; make the health system work better for everyone. So when it comes to how we use the world's large accumulation of health-related information, or guide health and lifestyle choices or manage pharmacy benefits for millions, our first goal is to leap beyond the status quo and uncover new ways to serve. Optum, part of the UnitedHealth Group family of businesses, brings together some of the greatest minds and most advanced ideas on where health care has to go in order to reach its fullest potential. For you, that means working on high performance teams against sophisticated challenges that matter. Optum, incredible ideas in one incredible company and a singular opportunity to do your life's best work. SM
Diversity creates a healthier atmosphere: UnitedHealth Group is an Equal Employment Opportunity/Affirmative Action employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, protected veteran status, disability status, sexual orientation, gender identity or expression, marital status, genetic information, or any other characteristic protected by law.
UnitedHealth Group is a drug-free workplace. Candidates are required to pass a drug test before beginning employment.
Skills Required
- Bachelor’s degree in Computer Science, Engineering, Math, or a related field, or equivalent experience
- 10+ years of professional software or ML engineering experience
- 3+ years leading projects or teams
- Hands-on experience with LLMs, embeddings, RAG, fine-tuning, and evaluation
- Familiarity with Hugging Face and LangChain or LlamaIndex
- SQL, Spark or Dask, and workflow orchestration with Airflow or Prefect
- Statistics and machine learning knowledge, including hypothesis testing, experimental design, feature engineering, and supervised and unsupervised methods
- Deep learning expertise with PyTorch or TensorFlow and modern NLP with transformers
- Expert Python and pandas skills, vectorized high-performance coding, testing practices, pytest, and Git
- Cloud proficiency with AWS, Azure, or GCP
- Containerization and orchestration with Docker and Kubernetes
- CI/CD and exposure to infrastructure as code with Terraform
- Excellent communication and stakeholder management skills
- Master’s or PhD degree
- Healthcare domain experience involving claims, EHR, HL7/FHIR, coding, risk adjustment, quality measures, or de-identification
- Experience with Databricks, Snowflake, BigQuery, Kafka, and lakehouse patterns
- Experience with MLOps tools such as MLflow, SageMaker, Azure ML, or Vertex AI
- Experience with vector databases, knowledge graphs, and healthcare ontologies
- Experience with SOC 2 or HITRUST security and compliance frameworks
- Experience with Scala, Java, or Go
Optum Compensation & Benefits Highlights
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Leave & Time Off Breadth — PTO is generally described as decent or good, and many note it as a strong part of the package. Actual ability to take time off can depend on workload and team coverage.
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Retirement Support — Offerings include a 401(k) with employer match and access to an employee stock purchase plan, which are highlighted as meaningful components of total rewards. These programs are consistently referenced among core benefits.
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Parental & Family Support — Parental and caregiver leave, along with adoption assistance, are publicly highlighted and viewed as notable elements of the package. Availability can be role-specific, but these supports contribute to overall breadth where offered.
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The Company
What We Do
Optum, part of the UnitedHealth Group family of businesses, is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. At Optum, we support your well-being with an understanding team, extensive benefits and rewarding opportunities. By joining us, you’ll have the resources to drive system transformation while we help you take care of your future. We recognize the power of connection to drive change, improve efficiency and make a difference in health care. Join a team where your skills and ideas can make an impact and where collaboration is key to creating technology that produces healthier outcomes.
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Optum Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
Optum has three workplace models that balance the needs of the business and the responsibilities of each role. These models, core on‑site (5 days/week), hybrid (4 days/week) and telecommute or fully remote, vary by country, role and location.
Typical time on-site:
Not Specified
