Top Machine Learning Jobs

The Staff Machine Learning Engineer will lead ML strategy, architect AI infrastructure, mentor engineers, and develop impactful AI systems.

Build and iterate ML-driven products and personalized recommendation systems for millions of users. Develop models, work with product and data science teams, run and analyze live experiments, and improve model quality. Collaborate across engineering and participate in in-person company events to support team culture.

Lead ML efforts for behavior and planning: design, train, and deploy state-of-the-art models (prediction, decision-making, RL/imitation, generative, transformers), own full model lifecycle from data to onboard inference, generalize behavior across geographies and platforms, collaborate cross-functionally, and mentor engineers.

Six-month on-site co-op building LLM-driven features and agents inside the Nuclear Operating System. Ship ML models and agent workflows, develop data pipelines (Foundry/AWS GovCloud), build predictive and anomaly-detection models on time-series sensor data, and own model quality, evals, and monitoring for production readiness.

Six-month on-site Fall 2026 co-op building ML and LLM-driven features and agents inside the Nuclear Operating System. Responsibilities include shipping models and agent workflows, building data pipelines and integrations (Foundry, AWS GovCloud), developing predictive/time-series models, and owning model quality, evals, and monitoring to keep AI features production-ready and audit-ready.

Build and ship ML and LLM-driven features and agents inside the Nuclear Operating System (NOS). Develop data pipelines and integrations (e.g., Foundry, AWS GovCloud), create predictive and anomaly-detection models, and own model quality with evals, regression suites, and monitoring. Work on production systems alongside engineers and domain experts during a summer on-site internship.

Design, build, and operationalize ML/AI models from large structured and unstructured data to produce actionable insights across functions. Collaborate with cross-functional teams, communicate results to technical and non-technical stakeholders, partner with MLOps for deployment, maintain documentation and reusable libraries, and apply statistical, optimization, and machine-learning techniques in production using CI/CD practices.

Design, build, evaluate, and deploy ML systems for search, recommendations, embeddings, and personalization. Work with large datasets to create features, train models, run experiments, monitor production models, and improve reliability, latency, and marketplace outcomes in collaboration with cross-functional partners.

Paid 6–12 month research internship on Epic's Special Projects & Research team. Build and evaluate ML models across vision, language, and audio to support player safety, game agents, world and content generation, and developer tools. Own data analysis and dataset creation, implement end-to-end models, run experiments, and collaborate with research scientists and engineers on deploying models at scale.

Design and implement agentic systems using LLMs and agent frameworks, build multi-agent workflows and RAG pipelines, optimize scalable agent architectures, collaborate on experiments and feature integration, and ensure quality through reviews, testing, and documentation.

Design, train, and deploy reinforcement learning systems for production decision-making. Develop reward models, use simulation environments and large-scale experience collection, train neural policies on GPU clusters, ensure stability and safety, and transition RL solutions from research into production.

Develop, prototype, and transition machine learning, statistical, and graph-based analytics into production. Build models and synthetic data, extract features from diverse data formats, produce visualizations, validate performance using standard metrics, and lead analytic efforts to scale solutions while partnering with software and cloud engineers.

Design, build, and productionize ML systems focused on information extraction, retrieval, ranking, and recommendation. Own full ML lifecycle from feature engineering to deployment, observability, and evaluation. Collaborate with data, infra, and product teams to scale serving for streaming and batch inference and improve models using dataset engineering and graph/embedding techniques.

Lead and build a research-focused ML team to develop and adapt advanced ML algorithms (post-training, RL, pretraining, sparsity, dataset curation) for deployment on Cerebras hardware, collaborate across teams and external partners, and guide production training, tuning, and evaluation to drive hardware-software co-design and performance optimization.

Implement and scale LLM training, fine-tuning, and post-training techniques (RL-based). Build evaluation and data pipelines, debug ML stack issues, optimize training/inference workflows, and ship maintainable ML infrastructure code.

Lead development of a high-performance robotics simulation platform and its compiler stack. Design compute infrastructure and data flow for physics simulation and model training, optimize GPU codegen and JIT compilation, improve differentiable programming support, and contribute to open-source ML compiler projects while collaborating with simulation and robotics engineers.

Design, implement, and productionize ML features and services using LLMs, RAG, and information-retrieval methods. Build scalable pipelines, evaluate and observe models in production, deploy APIs/services with Docker/Kubernetes, and lead technical work for ML solutions across HR and Finance product domains.

The Founding ML Research Engineer will design and evaluate ML models, develop experimentation pipelines, and contribute to AI research while optimizing training processes.

As a Machine Learning Intern, you'll design, train, and evaluate ML models, work on data preprocessing, develop pipelines, and assist with model deployment while collaborating with experienced engineers.

As a Founding ML Engineer, you'll develop ML models for lead optimization, automate demand generation processes, build data pipelines, and collaborate with teams to enhance growth through AI solutions.

As an ML Engineer - Robotics, you'll design, train, and deploy intelligent models for autonomous systems, focusing on perception, planning, and decision-making pipelines, while collaborating with robotics engineers to evaluate model performance.

The Founding ML Engineer will build and scale machine learning systems, focusing on ML pipelines, model implementation, and collaboration with teams to drive measurable impact.

Design and productionize ML models and agentic systems for intraday financial alpha generation. Build data pipelines, experiment frameworks, and portfolio optimization methods. Collaborate with quants and engineers to move models from research to live trading environments.

Design, train, and deploy ML models for Qur'ān recitation, tajwīd detection, and ASR. Build data pipelines for cleaning, segmentation, labeling, and evaluation. Integrate real-time predictions via APIs or lightweight on-device inference, optimize models for speed and mobile performance, and collaborate with backend developers to ship AI features in the app.

As a founding Machine Learning Engineer, you will improve core models, optimize LLM inference, develop data pipelines, and ship features directly to users.