Top Machine Learning Jobs

The Senior ML Systems Engineer will build and maintain the training framework for large-scale language models, focusing on distributed training and performance optimization.

Design, train, and deploy reinforcement learning systems for high-impact decision problems. Build and evaluate RL policies at scale, handle reward modeling and simulation, and productionize stable, safe RL solutions.

The role involves designing de novo frameworks for molecular engineering, collaborating across teams to optimize data-driven biological experiments and integrating ML models with experimental systems.

As a Machine Learning Engineer, you will design and train perception models for robotics, focusing on 3D perception systems. You will tackle complex physical challenges, optimize models for embedded hardware, and collaborate with cross-functional teams.

Build systems for Mercor's hiring engine, including pipelines for indexing, sourcing, scoring, and conversion. Work with ML and backend technologies.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

The Machine Learning Engineer will design, implement, and optimize reinforcement learning models for autonomous driving systems, enhancing data systems for scalable evaluation and performance assessment.

Design, train, and deploy multimodal machine learning systems that fuse 3D/vision, simulation, tabular, and text data. Own end-to-end model lifecycle from architecture and training to evaluation, production deployment, and monitoring, and build agentic workflows and retrieval/embedding capabilities to support underwriting and mitigation decisions.

Design, build, and deploy machine learning software and data pipelines for production government environments. Integrate and optimize distributed/cloud services, ensure code quality, lead reviews, mentor teammates, and engage directly with clients.

Responsible for developing machine learning capabilities, partnering with Data Scientists, and enhancing customer experiences through AI solutions.

Lead design and delivery of large-scale ML and AI systems for Airbnb Community Support products. Develop and productionize LLM-based solutions (fine-tuning, RAG/search, evaluation automation, feedback learning, guardrails), collaborate cross-functionally with product and design, and shape early-stage AI initiatives through to production to improve customer support experiences.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

Design, develop, and maintain real-time deep learning pipelines and production ML microservices on Kubernetes. Curate datasets, establish benchmarks, improve observability and monitoring, and scale ML workflows. Collaborate with product, backend, and systems teams, contribute to R&D for aerospace/defense use cases, and document operational best practices.

As an Open-Source Machine Learning Engineer at Hugging Face, you'll improve ML libraries, work with the community, and contribute to open-source projects.

As a Lead Machine Learning Engineer at Root, you'll build scalable ML systems, partner with data scientists, and improve operational practices for customer lifetime value modeling.

The Senior Machine Learning Engineer will develop and enhance ML systems for content resolution and work on multimodal understanding involving music, video, and metadata. Responsibilities include leading ML pipeline evolution, experimentation for quality improvements, and mentoring team members.

The role involves optimizing machine learning models for autonomous driving by implementing performance techniques and collaborating with cross-functional teams on architectural decisions.

Design, train, test, deploy, and maintain ML models for national-security cyber applications. Collaborate with software engineers and data scientists to build LLM-based automation, fine-tune open-source models, containerize solutions, and define ML strategy for mission-critical systems while meeting security requirements.

Design, train, test, deploy, and maintain ML models for defense customers. Collaborate with data engineers, scientists, architects, and product owners to implement deep learning, computer vision, and generative AI solutions and guide AI strategy for mission-critical systems.

Design, train, test, deploy, and maintain machine learning models for defense-focused solutions. Collaborate with data engineers, data scientists, and product teams to implement deep learning, computer vision, and generative AI use cases and support clients across the DoD market. Maintain model development and data handling pipelines.

The AI / Machine Learning Engineer will design, build, and deploy ML models for image classification, fraud detection, and data pipelines, while collaborating with product and engineering teams.

The Staff AI/ML Engineer will lead the development of AI systems for regulatory compliance, focusing on ML architecture, implementation, and oversight of model reliability.

The Machine Learning Engineer will execute the full ML lifecycle, collaborating on model implementation and improving customer experiences through data-driven insights.

Design and implement reinforcement learning algorithms for robotics, conduct experiments, optimize models, and collaborate with cross-functional teams.

Design, build, and deploy machine learning solutions. Collaborate with teams to transform research into practical applications using large datasets.