Top AI & Machine Learning Jobs

Architects, integrates, fine-tunes, tests, and deploys AI solutions using enterprise platforms, APIs, automation tools, and data services. Builds prototypes, predictive models, and production-ready workflows; evaluates accuracy, bias, groundedness, drift, security, and operational performance. Collaborates with business, platform, architecture, security, data, and vendor teams to define requirements, resolve issues, support adoption, and recommend scalable, maintainable AI capabilities.

Lead design and validation of AI-driven legal product capabilities: develop NLP/LLM and generative AI solutions (RAG, prompts), design agentic workflows, build retrieval/hybrid search strategies with embeddings and vector DBs, analyze large legal datasets, establish model evaluation best practices, translate results into product recommendations, collaborate with product and engineering, and mentor junior team members.

Own machine learning problems end to end, including data, modeling, evaluation, deployment, monitoring, and production optimization. Build training, inference, evaluation, datasets, and benchmarking pipelines for indoor positioning and item localization. Collaborate with applied scientists, engineering, and product teams to deliver enterprise features while optimizing accuracy, coverage, latency, and cost. Contribute reusable infrastructure, architecture decisions, and high engineering standards.

Leads Labcorp’s enterprise data and AI strategy, including governance, cloud modernization, platform engineering, analytics, interoperability, responsible AI, and technology value realization. Oversees development of scalable AI-ready data ecosystems, enterprise data products, engineering productivity, security, privacy, compliance, and organizational transformation. Partners with C-suite stakeholders and external organizations to deliver healthcare, research, operational, and financial outcomes while managing multidisciplinary teams, budgets, and global technology initiatives.

Conduct operations research and data science analysis for Department of Defense test and evaluation programs. Develop data-driven models, workflows, visualizations, notebooks, and interactive analytics using Python, R, SQL, and machine learning. Collaborate with technical and non-technical stakeholders, gather requirements, present findings, operate and adapt analytical tools, train users, troubleshoot within restricted networks, and support data product development. The role is remote with 25% travel and potential surges to 50% to support DoD ranges and laboratories.

Participate in AHEAD’s associate development program, completing technical training, certifications, and project shadowing before contributing to client data and AI solutions. Responsibilities include collaborating across teams, solving technical problems, analyzing issues, making recommendations, managing priorities, and delivering projects. The role requires foundational AI/ML knowledge, Python proficiency, familiarity with generative AI tools, vector stores, cloud platforms, containers, MLOps, and strong communication skills.

Supports data science and analytics teams by managing Jira requirements, backlogs, workflows, dashboards, model portfolios, and prioritization processes. Partners with technical teams, leadership, and stakeholders to communicate model status, risks, blockers, and priorities. The role also improves documentation and intake processes, facilitates prioritization decisions, and builds repeatable processes in a compliance-sensitive environment.

Conduct independent research on frontier AI evaluation and auditing. Design rigorous evaluations, audit methods, and reproducible experimental pipelines; analyze model capabilities, risks, robustness, and alignment-relevant behavior. Produce audit reports, recommendations, publications, tools, and presentations while collaborating with technical, policy, engineering, and standards teams. The role requires careful handling of confidential information and may involve pilot audits with frontier AI companies.

Designs and implements AWS cloud-native applications, distributed systems, and machine learning infrastructure. Leads architecture for SageMaker model deployment, inference, endpoints, and AI integrations while guiding engineering teams. Builds scalable event-driven services using AWS Lambda, SNS/SQS, EventBridge, DynamoDB, and Bedrock. Develops Python automation and ML integrations, applies Terraform and CI/CD, and ensures production ML workloads are secure, reliable, scalable, and cost-effective. Preferred experience includes NLP, embeddings, generative AI, OCR, MLOps, RAG, and vector databases.

Build and evaluate AI prototypes such as assistants, document extraction tools, retrieval experiences, and workflow applications. Develop lightweight interfaces and services, integrate APIs and approved data sources, design prompts, implement access controls, and create repeatable evaluations. Maintain version-controlled code, document prototypes, investigate technical issues, and refine solutions based on user feedback. Collaborate with technical and nontechnical stakeholders to determine whether prototypes are reliable enough for production pilots.