Top Senior Machine Learning Jobs

The role involves researching and developing probabilistic models for multi-sensor fusion and multi-target tracking to enhance real-time airspace awareness systems, as well as transitioning algorithms into production-ready systems.

The Senior ML Engineer will design evaluations for AI features, optimize pipelines, and ensure quality in ML systems while collaborating with cross-functional teams.

As a Senior Machine Learning Software Engineer, you will design, deploy, and optimize ML solutions in healthcare, working with AI scientists and ensuring compliance with regulations.

Design, train, and evaluate machine learning models to match patients with therapists, improving clinical outcomes and enabling tailored care.

The Senior Machine Learning Engineer will design and deploy ML models, analyze large datasets using PySpark, and collaborate across teams in a cloud environment.

The Staff Machine Learning Engineer will define and build ML capabilities, design production systems, and lead ML strategy in a high-growth AI cybersecurity company.

The Machine Learning Engineer role at Wayve focuses on leading initiatives in model-based autonomous driving, involving the development and delivery of ML-driven behaviors, collaboration across teams, and mentoring other engineers.

The ML Engineer will design and implement AI solutions for Axon's devices, collaborating with scientists to enhance model efficiency and ensure responsible AI deployment.

Design and build core ML systems for AI agents, managing the lifecycle from problem framing to deployment, ensuring solutions are scalable and enterprise-ready.

The Machine Learning Engineer will own the ML lifecycle, develop production models, monitor performance, and collaborate with cross-functional teams to enhance business onboarding and fraud prevention applications.

The Machine Learning Engineer will design and build systems that improve the AI shopping agent by using real user feedback, focusing on feedback loops and metrics to enhance user interactions and agent performance.

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.

Develop advanced ML solutions to improve Stripe's payment products. Work on the ML lifecycle from research to deployment, collaborating with product teams.

Design, implement, deploy, and monitor production-grade ML and computer vision models (CNNs, transformers, vision-language). Evaluate models, analyze data, improve robustness, support MLOps/CI-CD, and mentor junior engineers within an Agile team.

As a Senior Machine Learning Engineer, you'll lead the development of a neural welding simulator, integrating research and production to enhance robotic systems. Responsibilities include building predictive models, collaborating with reinforcement learning engineers, and translating prototypes into maintainable production code.

Lead a team of data scientists developing ML-driven products, managing infrastructure, training models, and ensuring production quality while collaborating with senior leadership.

As a Principal Decision Scientist, you'll utilize machine learning to provide insights and solutions, managing client relationships and leading projects from conception to deployment. You will architect solutions and oversee development, ensuring best practices in coding and model deployment.

Lead the development of ML models for autonomous vehicle behavior planning, mentor junior engineers, and advance machine learning technology for safe navigation.

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

Lead the development of machine learning models for maritime autonomy, focusing on Radar and EO/IR object detection and semantic segmentation. Collaborate on multi-target tracking and SLAM, and optimize models for edge deployment.

Lead ML engineering team to develop and manage AI/ML models and production systems, ensuring quality, compliance, and collaboration across teams.

Lead the design, development, and implementation of machine learning models to solve complex problems, collaborating with cross-functional teams to enhance services and deploy solutions in production environments.

The Principal Machine Learning Engineer will develop optimization algorithms, enhance LLM deployments, and collaborate with research teams on model performance and compression.

The role involves creating models for machine learning accelerators, analyzing workloads, and collaborating with engineering teams to optimize performance and functionality.