Top Machine Learning Jobs

The role involves defining and building ML capabilities, creating production systems, and managing data strategy while collaborating with engineers and leading an ML team.

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.

Contribute to end-to-end ML applications in finance, collaborating with investment professionals to deploy scalable AI solutions.

The Machine Learning Researcher at Virtu will develop and implement innovative deep learning models to solve complex market problems, collaborating with cross-functional teams and directly impacting business outcomes.

Design and deploy AI/ML solutions for real-world problems, collaborating with teams to build, evaluate, and integrate models into production systems.

The AI/Machine Learning Engineer will design, build, and optimize backend systems for AI agent workflows, focusing on Python services and integrating AI/ML frameworks for enterprise applications.

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.

Build and enhance ML systems for custom model training, oversee projects from data intake to model delivery, ensure model quality at scale, create evaluation frameworks, and collaborate with engineers.

As a Machine Learning Researcher, you will research and experiment with new architectures, optimize models, and validate findings through rigorous experiments, enhancing AI models for clients.

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.

Design, build, and maintain scalable AI/ML architectures; collaborate with data scientists, engineers, product, and compliance to integrate models into products; evaluate tools and ensure performance, quality, and compliance while staying current on AI/ML advances.

The Machine Learning Engineer will extract features from financial data, build predictive models, and enhance Clair's Data Science platform while collaborating with various teams.

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.

Own Coinflow's ML layer: explore transaction and behavioral data, build production models and decisioning systems, establish ML/data practices, define core metrics, and partner with Engineering, Product, and Operations to embed intelligence into products and tooling.

The Machine Learning Engineer will design and develop ML systems, implement algorithms, and improve product performance. They will work with data to build efficient self-learning applications.

Design and deliver customer experience solutions using machine learning techniques, analyze customer data, implement ML algorithms, and automate processes.

Design, develop, test, and deploy machine learning models and pipelines for manipulation, navigation, locomotion, and perception in humanoid robots. Monitor model performance in real-world settings, collaborate with partners and universities, and help grow the ML and autonomy teams.

Lead and build the companys ML function: define strategy, design production ML systems and infrastructure, own data and evaluation practices, write production code, integrate ML into products, and recruit and mentor an expanding ML team.

Define ML strategy and build production ML systems end-to-end (data pipelines, training, evaluation, inference). Own data strategy, integrate ML with product/backend, write production-quality code, and recruit and mentor the ML team while setting technical direction.