Top Senior Machine Learning Jobs

Lead end-to-end development of AI and machine learning solutions for a medical claims synthesis product. Design and deploy LLM-driven features, hybrid statistical ML and RAG systems, document extraction, and human-in-the-loop summarization workflows. Make architecture decisions, optimize production systems, evaluate model performance, collaborate with SMEs and product teams, and mentor engineers. Translate emerging AI research into reliable, scalable production applications.

Own the full lifecycle of clinical text extraction models, from problem framing and annotation strategy through production deployment, evaluation, monitoring, and retraining. Build clinical NLP pipelines, establish defensible quality and error-analysis systems, and apply LLMs using rigorous evaluation, observability, and cost controls. Partner with clinical data and backend engineering teams while addressing PHI protection, de-identification, auditability, and access controls. As the first ML hire, set technical direction and potentially build and lead the future ML team.

Design, train, and deploy ML models and pipelines to optimize Databricks serverless compute. Build end-to-end ML4Sys solutions, scale infrastructure, implement optimization and scheduling algorithms, and collaborate on product roadmap to improve performance and cost efficiency.

Contribute to development, deployment, and monitoring of production ML models; perform data cleaning, analysis, and visualization; collaborate with cross-functional teams to deliver AI-driven business solutions and communicate insights to stakeholders.

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.

Develop, evaluate, and deploy reinforcement learning methods for autonomous vehicle planning. Design real-time model architectures, build offline data-driven ML infrastructure, analyze fleet and field-testing data, optimize and deploy planning models, and collaborate with perception, software, and AI teams to deliver production autonomous driving systems.

Develop, optimize, and deploy computer vision and machine learning algorithms for autonomous robotics systems. Train custom models, including object detection, segmentation, and 3D vision architectures, for embedded platforms. Collaborate across teams to integrate algorithms into products, research emerging computer vision techniques, and maintain documentation and unit tests. The role requires strong PyTorch, C++, Python, multi-view geometry, deep learning, and robotics expertise.

Build and maintain scalable ML and generative AI platforms and backend infrastructure; deploy and manage production applications; create services exposing ML/AI products; implement RAG solutions and vector databases; integrate LLM orchestration and foundation model APIs; collaborate cross-functionally and mentor teammates.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

Develop and maintain a full-stack platform for computational chemists, focusing on drug discovery through machine learning and data infrastructure. Collaborate with interdisciplinary teams to enhance software tools for visualizing molecules and managing workflows.

As Director of AI/ML, lead the ML organization, set technical vision, mentor a high-performing team, and contribute to model development and production deployment.

Support USASOC data science efforts by developing data-driven and predictive ML solutions, evaluating data sources, building APIs and models, monitoring model performance, collaborating with stakeholders, and mentoring junior staff. Work is on-site at Fort Bragg and requires TS/SCI clearance and 5-8 years of experience.

Lead actuarial data scientist to oversee technical pricing models, actuarial systems, and a team of actuarial data scientists. Develop strategic roadmap, establish modeling standards and validation, apply ML/AI to pricing, collaborate with legal/compliance, present insights to executives, and manage projects to deliver high-quality actuarial products.

Conduct ML research for query understanding, ranking, clustering, and AI-powered search. Prototype and productionize algorithms, define data and evaluation requirements, design scalable systems, collaborate with engineers, run experiments, and publish patents/papers to integrate state-of-the-art ML into Yahoo Search products.

Design, develop, and deploy scalable ML software systems for healthcare. Collaborate with teams to improve products and ensure performance and reliability.

Design and develop scalable ML systems for healthcare, collaborating with teams to enhance products and maintain efficient infrastructures.

The Senior Machine Learning Engineer will design and deploy AI systems, optimize production models, and ensure efficient performance in resource-constrained environments.

The Senior Machine Learning Engineer will design, build, and deploy scalable ML systems, collaborating closely with teams to optimize AI processes and mentoring juniors.

The role involves designing and implementing machine learning systems for knowledge representation and temporal reasoning, focusing on organizational intelligence. Responsibilities include building LLM-powered pipelines, developing memory algorithms, and researching various ML techniques.

Develop and deploy lightweight machine learning models for edge AI, working on hardware integration and optimization techniques while mentoring junior engineers.

Lead design, development, and productionization of advanced ML models and data pipelines. Drive architecture, experimentation, validation, and best practices while mentoring ML engineers and delivering scalable, business-driving AI solutions.

Lead design, development, and deployment of advanced machine learning models and infrastructure. Own architecture, data pipelines, experimentation, validation, and team technical leadership to drive AI-driven product features and business value.

As a Senior Software Engineer, you'll build AI features focused on LLMs and RAG systems, collaborating across teams to ensure robust performance.

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.

Lead end-to-end ML projects for fraud detection: develop, deploy, monitor, and iterate production models and experimentation frameworks. Improve model performance, build scalable ML pipelines, collaborate with Product/Engineering/Risk, and champion testing, documentation, and model-monitoring best practices to ensure robust, high-impact solutions.