Top Machine Learning Jobs

Review and audit machine learning models and model risk practices across the enterprise. Partner with cross-functional teams to design, train, evaluate, validate, and implement ML and NLP solutions, using tools like Python, Spark, H2O, and AWS. Translate technical findings into business recommendations and assurance for stakeholders and the Audit Committee, while researching and applying emerging Generative AI and state-of-the-art methods.

Perform data analysis and test & evaluation of machine learning algorithms; develop algorithms for physical and autonomous systems; transition algorithms to software and embedded systems; work with stakeholders to present results and solutions; research new ML and data-analysis methods; handle large, multidimensional datasets and data pipelines.

Design, develop, and evaluate large-scale multimodal foundation models for biological research. Build hybrid LLM and graph neural network architectures, relational foundation models, and distributed multi-GPU training systems. Apply deep learning theory to biological data, transition research prototypes into scalable production systems, publish peer-reviewed research, and mentor junior staff while collaborating across scientific and engineering teams.

Build and operate Unity’s machine learning infrastructure, including data and feature pipelines, distributed training workflows, orchestration, model serving, monitoring, validation, and testing. The role involves delivering ML platform projects end-to-end, troubleshooting complex pipeline issues, improving reliability and reproducibility, and collaborating with ML engineers, researchers, managers, and partner teams.

Leads the design, development, and deployment of AI-powered solutions, intelligent automation, and digital workers. Prototypes and scales generative and agentic AI capabilities, integrates them with enterprise platforms and workflows, and establishes AI engineering, governance, security, privacy, and responsible AI standards. The role also leads technical workstreams, mentors engineers, evaluates emerging technologies, and drives enterprise adoption of innovative AI solutions.

Run and evaluate machine learning training experiments for autonomous robots, curate and clean real-world fleet data, improve data-labeling structures, track model performance metrics, and collaborate with Teleoperations to improve data collection. The role focuses on production ML for perception, decision-making, and control, including imitation learning, reinforcement learning, and deployment evaluation on live robotic systems.

Build and deploy scalable AI and machine learning products for clinical research, including patient recruitment, enrollment forecasting, and study feasibility. Develop ML models, LLM-powered agents, data pipelines, and MLOps infrastructure using cloud services and CI/CD automation. Collaborate with cross-functional teams to integrate AI into clinical research workflows, monitor model performance, and advance reliable, unbiased solutions that improve patient outcomes.

Develop and deploy production-ready machine learning models for game bots, intelligent NPCs, companions, world modeling, moderation, security, and QA automation. The role focuses on deep learning, computer vision, natural language processing, foundation models, reinforcement learning agents, reward design, policy optimization, data development, model evaluation, and scalable training environments. Candidates will collaborate with developers using agile practices and contribute to the roadmap for applied machine learning systems in gaming.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

Build and deploy video recommendation models and APIs, research LLM and computer vision applications, and develop semi-automatic annotation techniques for NLU models. Retrain and evaluate machine learning systems, improve Roku Voice data annotation pipelines, collaborate with engineering and product teams to launch research into products, and document technical work through design documents, demos, and presentations.

Leads the evaluation, design, development, and deployment of production AI/ML applications in AWS environments. Builds and optimizes machine learning models, assesses emerging technologies, presents technical recommendations, collaborates with federal clients, and mentors engineering teams. The role also supports cloud-native development, operations, security, enterprise AI adoption, and experimentation involving modern AI platforms and practices.

Designs, develops, integrates, evaluates, and maintains AI and machine learning capabilities for NATO operational requirements. Responsibilities include generative AI, NLP, predictive analytics, model training, validation, deployment, monitoring, API and pipeline development, lifecycle management, responsible AI, and collaboration with technical and operational teams.

Designs and integrates LLM and agent orchestration workflows, RAG pipelines, vector databases, memory services, MCP tool integrations, and AI governance controls. Evaluates and optimizes models, routing, agent behavior, safety, and performance through automated benchmarking and observability. Partners with data scientists and mission analysts to productionize AI capabilities for DoD mission environments and researches emerging AI technologies.

Design, build, and deploy production machine learning systems across workflow automation, data enrichment, predictive insights, and agent-based workflows. Own the full ML lifecycle, including data ingestion, model training, evaluation, deployment, and feedback loops. Integrate AI into backend services, APIs, data pipelines, and customer-facing products while shaping architecture and long-term AI strategy. Collaborate with product, engineering, and customers to deliver scalable, reliable AI solutions.

Leads and develops a perception and machine learning engineering team building computer vision, sensor fusion, tracking, detection, and scene-understanding capabilities for autonomous systems. Owns roadmap planning, technical decisions, delivery, model development and deployment workflows, field readiness, hiring, performance management, and cross-functional execution with autonomy, embedded, data, hardware, product, programs, and field operations teams.

Own the strategy, design, implementation, and productionization of machine learning capabilities for an unpublished R&D game project. Build reusable training and evaluation pipelines, apply reinforcement learning to improve decision-making, establish research-to-production and reliability practices, guide experimentation, and strengthen engineering standards. Collaborate across engineering, design, data science, and analytics while mentoring engineers and addressing technical risks.

Design, build, and maintain optimization technology, full-stack systems, and data-intensive scheduling infrastructure. Improve optimization algorithms using linear optimization, machine learning, and generative AI. Lead complex technical initiatives, establish testing and observability, support scalable operations, troubleshoot customer issues, participate in on-call rotations, and mentor engineers while partnering closely with product and engineering teams.

Develop and deploy machine learning models for lifecycle optimization, personalized notifications, and monetization. Collaborate with senior engineers, data scientists, and product managers to improve user experience and marketplace metrics. Work with embedding retrieval, Graph Neural Networks, ranking systems, experimentation, production deployment, and responsible AI practices including fairness, explainability, and quality.

Conduct ML research to produce predictive trading signals, design deep learning architectures, apply LLM-based agentic methods, implement signal compression and combination, build model frameworks for investment use, and collaborate with portfolio managers and researchers.

Designs and implements enterprise-scale AI and machine learning architectures and applications to solve complex client problems. Develops scalable software, processes structured and unstructured data, operationalizes ML platforms, and creates innovative solutions with minimal supervision. Collaborates cross-functionally, supports platform monitoring and operations, and mentors employees. The role requires TS/SCI clearance and may involve full-stack development, model deployment, distributed computing, streaming data, and data warehousing.

Lead technical direction for Pinterest’s foundational machine learning capabilities in visual representation learning, multimodal content understanding, and LLM-based personalization. Drive initiatives from research and experimentation through productionization and adoption. Build reusable embeddings, models, semantic signals, and ML infrastructure using distributed training and data pipelines. Collaborate across product, engineering, data science, and applied science while mentoring technical teams and guiding multi-quarter roadmaps.

Designs and integrates AI/ML models, agents, and workflows for secure, enterprise-scale mission capabilities. Responsibilities include developing agent orchestration, RAG pipelines, vector database integrations, MCP tool brokers, model routing, evaluation and benchmarking pipelines, observability, governance guardrails, and production AI services. Partners with data scientists and mission analysts to operationalize prototypes and researches emerging AI tooling.

Designs and integrates production AI/ML systems for a secure enterprise AI platform supporting cyber and national mission operations. Responsibilities include building LLM agent workflows, RAG pipelines, vector database integrations, model routing, MCP tool brokers, evaluation frameworks, observability, and governance guardrails. The role partners with data scientists and mission analysts to productionize prototypes and research while evaluating emerging AI technologies.

Design and integrate production AI/ML systems for USCYBERCOM, including LLM agents, orchestration workflows, model routing, RAG pipelines, vector databases, MCP tool integrations, evaluation frameworks, and governance guardrails. Partner with data scientists and mission analysts to transition prototypes into secure, enterprise-scale capabilities while evaluating emerging AI technologies and managing operational risks.

Own the design, provisioning, operation, and optimization of cloud-based GPU clusters supporting large-scale model training and inference. Partner with AI engineers to improve training performance and hardware utilization, contribute to ML libraries and infrastructure tooling, develop GPU compute strategies, optimize multi-cloud capacity and costs, and improve reliability, testing, documentation, and engineering practices.