Top Senior Machine Learning Jobs

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.

Design and scale advanced ML models and systems for prediction and recommendation. Mentor junior engineers while leading projects occasionally.

As a Staff Machine Learning Engineer, you'll lead AI product development, leveraging LLMs and embeddings, collaborating on technical strategy and coding performance enhancements.

The Senior Manager for Machine Learning will lead the ML team, coordinate initiatives, and enhance product development in a collaborative environment.

Lead roadmap and product strategy for vehicle autonomy. Define autonomy behavior, UX, data and ML lifecycle, benchmarks, and deliver cross-functional capabilities from data collection through deployment and monitoring.

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.

Lead experimental ML initiatives: design, train, and deploy multi-modal models and production pipelines; evaluate and prototype emerging ML technologies and agentic workflows; establish AI governance, mentoring, and knowledge sharing; translate research into business impact and thought leadership.

The Senior Machine Learning Engineer will design and deploy scalable machine learning models, optimize performance, and lead best practices while collaborating with cross-functional teams.

The Senior Machine Learning Engineer at DraftKings will design scalable ML pipelines, optimize models, implement monitoring systems, and lead technical initiatives in AI development.

The role involves leading ML project implementations, designing production-grade ML solutions, and optimizing AI applications for clients while ensuring effective communication and project management.

Lead and inspire a team to develop advanced threat detection algorithms using machine learning and deep learning, defining vision and strategy for product realization.

Design and build the data and analytics layer: schemas, pipelines, APIs, dashboards, forecasting and ML models. Deliver usage-based metering, ensure data correctness and observability, and ship customer-facing analytics while raising engineering standards.

Design, develop, test, and deliver new features for Red Hat OpenShift AI. Build open-source machine learning and data-science pipeline software, perform full-stack TypeScript/Node.js/React development on OpenShift, run unit and end-to-end tests, participate in code reviews and agile ceremonies, contribute to patents, and engage with the open-source community.

Build and own production applied-AI systems focused on information retrieval and LLM pipelines. Design experiment frameworks and evaluation pipelines, optimize for metrics (precision, recall, F1), run benchmarks, perform failure analysis, and iterate to improve real-world system performance at scale.

The role involves developing quantization and compression strategies for AV models, conducting numerical sensitivity analyses, and creating automated tooling for low-precision deployments, emphasizing safety and performance.

Lead end-to-end ML and perception efforts for future sensing in autonomous vehicles: design sensor architectures, multi-modal fusion, model development, metrics, simulation-driven evaluation, system integration, and production readiness while mentoring engineers and building scalable data and training infrastructure.

Develop and evaluate ML and perception models for future sensing and autonomous driving. Work on detection, tracking, classification, sensor fusion, training pipelines, validation, robustness metrics, and integrate models into onboard systems while collaborating with cross-functional teams.

Design, build, and deploy transformer-based ML and LLM systems to automate risk investigations and decisioning. Own end-to-end pipelines (data ingestion, prompt tuning, orchestration, productionization, monitoring) and collaborate with product, engineering, and operations to deliver copilot/autopilot workflows that improve analyst productivity and eliminate manual reviews.

Lead the design and advancement of AI platform infrastructure, focusing on distributed training, efficiency, and optimization of ML models. Collaborate with teams and guide architectural decisions.

The Senior ML/AI Engineer will enhance developer productivity for AV engineers at GM by designing, implementing scalable, reliable systems and mentoring team members.

The AI/ML Engineer will design, develop, and deploy machine learning algorithms and large language models while collaborating with stakeholders to meet business needs.

The role involves building and deploying ML models for core products, solving real-world problems, and collaborating with teams on ML-driven features.

The Senior AI ML Engineer will design and implement AI/ML systems for healthcare, manage projects from ideation to release, and mentor engineers. Responsibilities include delivering quality code, driving architectural decisions, and collaborating with cross-functional teams.

Design and implement backend and systems-level software for defense and intelligence, integrating signal-processing and machine-learning algorithms into real-time, field-ready systems. Collaborate in small teams to prototype, architect, and deploy extensible solutions that integrate with existing infrastructure while translating customer requirements into development priorities.

Develop and implement advanced reinforcement learning methods and distributed systems for AI agents. Collaborate with customers to understand needs and create tailored solutions.