Top Senior Machine Learning Jobs in Minneapolis, MN

Design, build, evaluate, and productionize machine learning and LLM-based agentic systems for healthcare products. Own projects end-to-end, including problem framing, dataset development, modeling, evaluation, deployment, monitoring, and safety guardrails. Build ML infrastructure, data pipelines, inference systems, and evaluation frameworks. Collaborate with clinicians, product managers, and engineers, while setting technical direction and mentoring team members.

Designs and implements enterprise cloud and AI solutions for unstructured data workloads. Responsibilities include building ingestion pipelines, API integrations, document extraction and HITL workflows, tuning machine learning models, benchmarking performance, and ensuring security, availability, disaster recovery, and data isolation. The role also supports customer developers, leads technical proofs of concept, mentors engineering teams, develops reusable blueprints, and provides feedback to product engineering.

Designs, deploys, and operates production machine learning systems for identity resolution, scoring, deliverability, and personalization. Responsibilities include feature engineering, model training, real-time inference, graph database integration, monitoring, drift detection, retraining, low-latency APIs, and scalable data processing. The role also contributes to MLOps standards, GPU acceleration, and ML architecture while partnering with platform engineering.

Own and improve Headway’s privacy program across HIPAA, 42 CFR Part 2, state privacy laws, individual rights, incidents, vendor governance, and AI use. Maintain controls, data maps, metrics, training, and operational workflows. Conduct product, vendor, and AI privacy assessments; manage rights requests and incident investigations; coordinate notifications and regulatory submissions; and translate complex requirements into practical guidance for cross-functional teams.

Lead Risk Operations transformation programs from problem definition through implementation and measurement. Own automation initiatives, process redesign, tooling improvements, partner transitions, operational metrics, readiness, and continuous improvement. Collaborate with Risk Operations, Fraud Strategy, Compliance, Product, Engineering, Data, Shared Services, and Vendor Management to deliver measurable gains in service levels, quality, risk outcomes, backlog, and cost.

Analyze product, user, transactional, and repayment data to develop risk and underwriting models, reduce losses and fraud, monitor model performance, and guide product and growth strategy. Build Looker dashboards and LookML models, conduct segmentation and experimentation, measure CAC and LTV, and communicate actionable insights to product, engineering, marketing, finance, and leadership.

Architect and implement enterprise-grade MLOps/LLMOps platforms on AWS and Kubernetes. Design ML/LLM pipelines for training, validation, deployment, monitoring, governance, CI/CD, and observability. Serve as technical authority, conduct architecture reviews, ensure security and compliance, and mentor engineering teams across projects.

Own the production lifecycle of ranking and recommendation models, including training, validation, deployment, monitoring, retraining, versioning, rollback, and retirement. Build scalable Spark-based feature and data pipelines, orchestration workflows, model hubs, observability, CI/CD, and reliability standards. Partner with data science and engineering teams to improve model quality, deployment speed, infrastructure efficiency, and operational resilience while mentoring teammates.

Build and scale accounting software synchronization solutions connecting BILL’s payment network with third-party accounting platforms. Lead end-to-end software design and delivery, develop scalable AWS microservices, improve platform performance, guide testing and CI/CD practices, resolve customer problems, mentor engineers, and use generative AI to accelerate development.

Lead the design, development, deployment, and operation of production-grade generative AI and LLM solutions. Build data and ML pipelines, fine-tune and evaluate models, implement RAG and prompt engineering, and establish MLOps practices on AWS. Drive use cases across insurance workflows, ensure security and responsible AI compliance, mentor engineers, and collaborate with product, engineering, security, finance, and go-to-market teams.

Drives underwriting innovation by designing, testing, and implementing new tools, technologies, strategies, and processes. Coordinates projects, manages deadlines and risks, supports user acceptance testing, and collaborates with IT, underwriting, data, product, operations, reinsurance, and vendors. Uses data analytics and machine learning to improve risk assessment, efficiency, and customer experience while ensuring regulatory compliance. Provides training and helps translate innovation concepts into adopted underwriting capabilities.

Lead development and productionization of telematics pricing and underwriting models. Responsibilities span statistical modeling, feature engineering, dataset and pipeline development, model deployment, monitoring, data quality validation, troubleshooting, regulatory support, and collaboration with pricing, actuarial, product, engineering, and compliance teams.

Own product positioning, messaging, segmentation, go-to-market strategy, competitive intelligence, customer proof, sales enablement, and thought leadership for Toloka’s enterprise AI data solutions and self-service platform. Translate complex AI and machine learning capabilities into differentiated customer value while partnering with product, sales, marketing, and customers.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Define architecture and metrics for autonomous driving evaluation, lead cross-functional integration across autonomy and simulation teams, develop statistical and machine learning methods, analyze large-scale time-series data, manage evaluation KPIs and release gates, and communicate safety and readiness insights through dashboards and stakeholder presentations.

Design and deliver enterprise-grade generative AI solutions on AWS using Bedrock, AgentCore, SageMaker, LLMs, RAG pipelines, vector databases, and agentic workflows. Build scalable APIs and integrations, optimize prompts and models, evaluate performance, and implement security and governance. Collaborate with application, data, and platform teams, troubleshoot production systems, define best practices, and mentor engineers while remaining hands-on.

Executes systems engineering across R&D platforms, hybrid cloud, SaaS, HPC, containers, integrations, data pipelines, observability, and AI architectures. Coordinates Enterprise IT, vendors, cybersecurity, compliance, and scientific stakeholders to deliver secure, scalable, validated, and supportable solutions. Develops technical documentation, deployment plans, runbooks, monitoring, lifecycle artifacts, risk logs, and operational handoffs. Implements RAG, vector database, AI gateway, agentic workflow, and multimodal integration patterns while supporting cloud migration, change management, governance, and technical issue resolution.

Leads application engineering teams delivering cloud-native, AI-enabled solutions on Microsoft Azure for Medicare and Medicaid claims-processing operations. Oversees architecture, software development, quality automation, integrations, support, governance, security, vendor relationships, and large-scale implementations. Develops Azure applications as needed, mentors staff, manages cross-functional initiatives, ensures HIPAA compliance, and partners with business and executive stakeholders to improve healthcare payer outcomes.

The Enterprise Account Executive drives 100% new-business growth across enterprise B2B accounts. Responsibilities include prospecting, developing territory and account plans, understanding client needs, presenting Conga’s SaaS solutions, managing opportunities through the sales cycle, building partner relationships, and closing high-value deals. The role collaborates with sales engineering, marketing, customer success, professional services, and other teams to achieve revenue and quota targets.

Develop and deploy machine learning models for fraud detection and credit risk decisioning. Own the full ML lifecycle, including data exploration, feature engineering, experimentation, validation, deployment, monitoring, and optimization. Analyze fraud trends and portfolio outcomes, collaborate with Product, Engineering, and Risk Operations, apply generative AI tools, and mentor fellow scientists while delivering measurable improvements in fraud prevention and responsible credit access.

Own the complete machine learning model lifecycle, from experimentation and validation through deployment, monitoring, retraining, rollback, and retirement. Build reusable training, evaluation, deployment, orchestration, and CI/CD infrastructure. Establish model promotion standards, versioning, lineage, monitoring, alerting, and incident-response processes. Partner with data scientists to preserve scientific validity, improve reliability and efficiency, support delayed-label use cases, mentor teammates, and define MLOps priorities.

Develop novel AI approaches for healthcare using structured and unstructured data, build ML models, conduct experiments, and collaborate with stakeholders.

Own the end-to-end computer vision platform for fine-grained item identification and pricing. Responsibilities include designing and fine-tuning vision models, defining evaluation and confidence standards, building labeling feedback loops, prioritizing coverage versus accuracy, and deploying reliable, low-latency serving infrastructure at scale. The role also communicates model capabilities and trade-offs to technical and non-technical stakeholders.

Build and operate distributed GPU training platforms for foundation models. Run and debug multi-node PyTorch and Ray Train workloads, tune GPU networking and scheduling, profile performance, and develop benchmarks and evaluation harnesses. Partner with infrastructure teams and client scientists to improve throughput, reliability, cost efficiency, observability, and end-to-end training pipelines on GKE.

Design, develop, deploy, and scale AI-powered solutions, digital workers, intelligent automation, and enterprise AI capabilities. Build Python-based generative and agentic AI solutions using LLMs, vector databases, embeddings, semantic search, and knowledge retrieval. Integrate AI with enterprise platforms and workflows, establish engineering and responsible AI standards, evaluate emerging technologies, and lead innovation pilots in collaboration with business, architecture, engineering, and operations teams.