Top Machine Learning Jobs

Design, develop, and deploy machine learning and deep learning models; build end-to-end ML pipelines including preprocessing, training, evaluation, and deployment; perform feature engineering on large datasets; deploy and monitor models on cloud platforms; collaborate with cross-functional teams to integrate AI solutions and retrain models as needed.

Design, develop, and deploy end-to-end machine learning models using statistical and ML algorithms. Prepare and enrich data, implement scalable PySpark/Databricks solutions, and collaborate with product and engineering to create personalized customer experiences and actionable insights.

Design, build, and deploy production ML models and scalable pipelines to improve recommendations, ranking, and personalization. Own end-to-end workflows from data exploration and feature engineering to training, evaluation, experimentation (A/B tests), and production deployment while applying responsible AI practices.

Lead technical strategy and build production-grade recommendation, ranking, and personalization ML systems. Design, deploy, and scale models and ML pipelines (Spark, Airflow), develop/fine-tune LLMs and AI Agents, run experimentation (A/B tests), mentor senior ICs, partner cross-functionally, and embed responsible AI practices.

The role involves building and deploying machine learning models, collaborating with engineers and product teams, and enhancing customer experience through ML solutions.

The Senior Machine Learning Engineer will design, implement, and scale advanced foundation models, collaborate with researchers, and build AI systems. Requires hands-on experience with distributed training pipelines and proficiency in AI frameworks.

Design, implement, and optimize machine learning and deep learning models; preprocess and analyze large datasets; integrate models into production; monitor and maintain model performance; collaborate with engineers and stakeholders; stay current with ML/AI advances.

Early-career ML engineer who builds, evaluates, and improves LLM-powered financial applications. Design and implement ML models, test and analyze LLM failure modes, apply statistics and experimental design, collaborate with cross-functional teams, ensure responsible AI practices, document results, and support junior teammates.

Design, implement, and optimize machine learning and deep learning models; preprocess and prepare large datasets; integrate models into production with software engineers; monitor and maintain production model performance; troubleshoot issues; and collaborate with stakeholders while staying current on ML/AI advances.

Develop, deploy, and optimize machine learning models (object detection, signal processing, predictive analytics) and data pipelines. Perform model evaluation, performance testing, and system-level debugging. Collaborate with engineers to integrate AI/ML into mission-critical systems, document designs, and support validation, verification, and safety/compliance activities.

Design, build, and deploy scalable AI and machine learning solutions, leading full AI lifecycle, collaborating with teams, and ensuring systems align with client needs.

Define and lead modeling architecture for personalization, matchmaking, recommendations, and social discovery. Build large-scale real-time inference systems, develop models for skill, behavior, trust & safety, and multi-objective optimization. Drive Responsible AI, experimentation, observability, and cross-functional standards while mentoring senior engineers and influencing ML strategy.

Design, build, deploy, and operate production-grade AI/ML and LLM systems (including RAG and agentic pipelines). Integrate AI capabilities with product workflows, run evaluation and monitoring, optimize model and system performance, and apply strong engineering practices (testing, CI/CD, versioning).

Lead development of machine learning, computer vision, sensor fusion, and registration algorithms for a robotic lung biopsy platform. Prototype, evaluate, and productionize image-analysis solutions in Python/C++, collaborate cross-functionally, perform failure-mode assessment, and maintain state-of-the-art technical knowledge.

Design and implement production ML features, close behavior gaps with common frameworks, investigate divergences, improve performance and stability, and document designs and tests for geospatial and large-scale ML workflows.

Build and productionize distributed pre-training stacks (PyTorch/JAX) at multi-node GPU scale, implement new optimizers and high-performance CUDA/Triton kernels, optimize mixed-precision training and memory/communication, develop experiment infrastructure and monitoring, and mentor junior engineers to accelerate foundational model research.

Customer-facing technical role supporting AI/ML initiatives across US Major Accounts. Architect and prototype AI/ML solutions, build MVPs and PoCs, support regional SE teams, deliver technical presentations and workshops, and upskill teams. Travel ~30-40% to support customer engagements.

Develop, prototype, evaluate, and deploy ML-first behavior prediction and planning models to forecast motion and interactions of road users. Build software and infrastructure for behavior-specific ML, collaborate with perception and planning teams, and productionize models for autonomy systems.

The Senior Analyst in Predictive Analytics and Machine Learning develops and maintains statistical models to support business decisions, monitors model performance, and collaborates with stakeholders across the company.

Develop and optimize a high-speed GPU-based simulation framework for ML training and validation. Apply reinforcement learning to solve behavior and path-planning challenges, bridge sim-to-sim and sim-to-real fidelity gaps, enable self-serve data generation for autonomy users, and write production-ready code integrating ML into core simulation architecture.

Lead technical development of machine learning algorithms for the onboard Planner, advancing imitation learning, reinforcement learning, and model scaling; integrate foundation models; create metrics, data, and training frameworks; mentor ML developers; and collaborate across perception, simulation, system design, and safety teams to solve autonomous driving challenges in complex urban environments.

The role involves developing and deploying machine learning solutions, optimizing models, and collaborating with cross-functional teams. Responsibilities include cloud architecture, algorithm engineering, and infrastructure best practices.

Build and maintain Lyft's ETA prediction system: analyze data, prototype ML and non-ML solutions, develop statistical/ML/optimization models, trade off accuracy versus production/runtime, write scalable production-quality code, participate in design/code reviews, on-call support, and incident triage.

Own the ML lifecycle for LLMs applied to manufacturing data: fine-tune and deploy models for document classification, information extraction, table parsing, and multi-page reasoning; build annotation tooling, active learning, and synthetic data pipelines; develop evaluation frameworks; collaborate on AI platform roadmap and optimize model accuracy and production health.

Develop hybrid, process-informed ML models and virtual sensors with process engineers; own end-to-end data science from feature engineering to production deployment, monitoring, and maintenance; engage customers and cross-functional teams for adoption.