Top Machine Learning Jobs

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.

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.

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.

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

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.

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.

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.

Design, build, deploy, and operate production-grade ML systems and pipelines for a massive consumer audience. Own full lifecycle from data processing and feature engineering through training, validation, deployment, and monitoring. Drive ML infrastructure and architecture decisions, partner with product and data teams, optimize models against business metrics, and mentor engineers to ensure security, reliability, and compliance.

Morgan Stanley seeks a Machine Learning Researcher to solve critical problems with innovative research. Responsibilities include applying machine learning techniques and strong communication skills within a collaborative team. Ideal candidates have 4+ years of research experience and proficiency in statistical learning methods.

As a Staff Machine Learning Engineer, you will design and implement AI-driven solutions, build ML infrastructure, and lead teams to deliver scalable systems for real-time recommendations.

Design, train, test, deploy, and maintain ML models (including generative and agentic AI) for DoD missions. Collaborate with engineers, scientists, and product owners to implement solutions, handle data and model development, and advise clients on ML strategy.

Design and build data and model layers for an accredited AI decision-support system: record matching, calibrated scoring, provenance-tracked retrieval and generation, secure ingestion pipelines, model packaging/serving, and instrumentation for audit and accreditation.

Lead end-to-end personalization and recommender system development: define technical roadmap, build and deploy scalable low-latency models, establish experimentation and monitoring, drive cross-team technical decisions, and mentor senior/mid-level scientists to improve recommendation quality and business outcomes.

Lead development of advanced ML models and production-grade systems to optimize pricing, allocation, and auctions in Roku's ad serving platform. Translate business problems into hypotheses, run live experiments, mentor teams, and collaborate cross-functionally to drive marketplace intelligence and demand optimization.

Lead end-to-end forecasting ML systems: scope, feature engineering, model development, backtesting, deployment, monitoring, retraining, and reliability improvements. Build production-safe evaluation infrastructure, AI-assisted development workflows, probabilistic/uncertainty-aware forecasts, and productize versioned forecast outputs. Partner cross-functionally to prioritize roadmap, define business-tied success metrics, and communicate assumptions and risks to stakeholders.

Lead design, development, testing, and deployment of cloud-enabled AI/ML systems (computer vision, sensor fusion, decision support) for maneuver, surveillance, and engagement missions. Evaluate model performance on operational datasets, optimize for robustness and cyber resilience, troubleshoot deployed systems, and produce technical documentation and reports for stakeholders.

Develop, deploy, and support secure, scalable production software and AI/ML solutions. Collaborate with product, UX, and engineering to deliver features, create tests and automation, instrument monitoring and dashboards, and participate in agile processes while continuously learning and improving team practices.