Top Machine Learning Engineer Jobs

Own the architecture and production delivery of a novel AI-powered exploitability engine. Lead probabilistic ranking, graph modeling, entity resolution, calibration, validation, and security guardrails for agentic systems. Drive zero-to-one technical direction, architecture, code reviews, cross-functional partnerships, and mentorship. Build scalable systems using distributed technologies, cloud-native development, APIs, databases, and machine learning while establishing defensible engineering decisions and evaluation metrics.

Leads the technical vision and architecture for AI-powered exploitability analysis in cybersecurity. Defines advanced modeling approaches, enterprise-scale engineering standards, AI safety and governance practices, and portfolio-wide architecture. Guides multiple teams without direct authority, mentors senior engineers, represents technology to executives and customers, and delivers production AI/ML products using LLMs, agents, retrieval, probabilistic scoring, and graph technologies.

Design, develop, deploy, and scale AI-powered solutions, digital workers, intelligent automation, and enterprise AI capabilities. Build Python-based generative and agentic AI solutions using LLMs, vector databases, embeddings, semantic search, and knowledge retrieval. Integrate AI with enterprise platforms and workflows, establish engineering and responsible AI standards, evaluate emerging technologies, and lead innovation pilots in collaboration with business, architecture, engineering, and operations teams.

Lead enterprise AI/ML engineering transformation by designing and scaling solutions with Azure OpenAI, LLMs, RAG, and agentic frameworks. Establish AI development lifecycle standards, responsible AI governance, security and compliance controls, and cloud-native architecture. Build engineering agents for automation, testing, code review, and releases; collaborate with business and technology leaders; mentor engineers; and track productivity, quality, delivery, and adoption metrics.

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.

Leads the AI/ML engineering roadmap and provides technical direction across machine learning, generative AI, agentic AI, NLP, and predictive analytics. Designs, trains, evaluates, and deploys production models; builds LLM, RAG, and AI-agent applications; establishes MLOps pipelines, monitoring, governance, responsible AI, and cloud infrastructure. Mentors engineers and data scientists, partners with business leaders, and communicates solutions and outcomes to executives.

Own numerical analysis and model parity for compressed, quantized, and optimized autonomous-vehicle models. Develop Jacobian/Hessian sensitivity tools, connect numerical differences to driving behavior, diagnose training instabilities, and build low-overhead observability for distributed training. Investigate floating-point errors, edge cases, and implementation divergence while translating analytical results into deployment decisions and production-ready tooling.

Design, architect, deploy, and optimize advanced machine learning models for real-time onboard autonomous driving. Lead end-to-end ML projects, improve neural architectures and inference performance, ensure robustness and safety in critical systems, mentor engineers, collaborate across engineering teams, and influence technical roadmaps. The role focuses on foundation models, computer vision, sensor-to-action learning, and onboard autonomy.

Develop and deploy machine learning systems for humanoid robot manipulation, covering data collection, annotation, synthetic data generation, model training, simulation, hardware deployment, and fine-tuning. Diagnose vision, calibration, sensor fusion, model, and data bottlenecks to improve reliability. Build real-time robotic software using modern computer vision and deep learning, while mentoring engineers and driving architectural excellence across the robotics organization.

Develop and maintain production AI/ML applications using agent-based workflows, LLM integration, RAG pipelines, REST APIs, and Python services. Design agent architectures, process structured and unstructured data, refine PostgreSQL schemas, and optimize applications for performance, reliability, scalability, and usability. The role also supports full-stack development, enterprise deployment, tool integration, memory management, vector search, and cloud or containerized environments.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Develop and deploy production AI/ML and agentic AI systems for pricing, marketing, personalization, and customer analytics. Build LLM agents, orchestration workflows, ML pipelines, and scalable infrastructure while applying software engineering, model validation, monitoring, governance, and responsible AI practices. Collaborate with product, business, data science, and engineering teams to translate ambiguous problems into measurable solutions, communicate technical trade-offs, and support junior team members.

Lead end-to-end machine learning development for autonomous driving, including data curation, foundation-model pre-training, post-training, fine-tuning, alignment, and deployment. Build scalable pipelines using real and synthetic datasets, apply imitation and reinforcement learning, improve model safety and reliability, collaborate across teams, lead technical initiatives, and mentor ML engineers.

Lead development of scalable, reliable continuous integration infrastructure supporting autonomous-vehicle development, machine learning training, simulation, and remote builds. Specialize in Remote Build Execution and a FUSE-based file system, enabling source-code editing and developer workflows. Design and implement productivity improvements, evaluate technologies, influence technical roadmaps, establish engineering best practices, manage technical debt, and mentor engineers while balancing business and customer priorities.

Develop and test machine learning models, pipelines, agentic systems, and LLM-powered applications under senior-engineer mentorship. Interns will work across the ML lifecycle, including problem framing, model selection, pipeline development, evaluation, and deployment support. The role involves collaborating with ML Engineers, Product Managers, Designers, and Full-Stack Engineers on scalable AI solutions.

Build and evaluate machine learning models, agentic systems, LLM applications, and pipeline components under senior-engineer guidance. Interns will gain experience across the ML lifecycle, including problem framing, model selection, development, evaluation, and deployment support. The role involves collaborating with ML Engineers, Product Managers, Designers, and Full-Stack Engineers on scalable AI solutions.

Lead the design and delivery of production machine learning systems for infrastructure observability, troubleshooting, and optimization. Develop anomaly detection, time-series analysis, event correlation, recommendations, ranking, and root-cause inference capabilities. Build evaluation frameworks, data pipelines, feature workflows, model-serving services, monitoring, and feedback loops. Integrate ML with telemetry platforms and customer-facing products, establish engineering standards, mentor engineers, and influence technical roadmaps across teams.

Develop and evaluate machine learning models for object classification, anomaly detection, and data-driven space mission tasks. Support data ingestion, preprocessing, feature engineering, experimentation, and model evaluation. Write clean, tested Python code while collaborating with senior engineers. The role is fully onsite in Denver or Long Beach and requires compliance with U.S. ITAR technology export regulations.

Develop, train, and evaluate machine learning models for space-domain applications including object classification and anomaly detection. Build data ingestion, preprocessing, feature engineering, and model evaluation pipelines; conduct experiments; track results; and write clean, testable Python code. Collaborate with senior engineers while contributing to AI capabilities supporting space security and autonomous systems.

Leads the design, development, deployment, and operation of production AI/ML solutions, including generative AI, RAG, agentic workflows, conversational AI, predictive models, and automation. The role establishes responsible AI practices, integrates enterprise data and APIs, improves evaluation and observability, partners with cross-functional teams, mentors engineers, and drives initiatives from proof of concept through production scale.

Leads the architecture, development, and deployment of AI/ML integrations and scalable event-driven backend systems for distributed clinical applications. Builds Kafka-based services, secure APIs, database integrations, CI/CD pipelines, containerized cloud deployments, and observability platforms. Develops agentic AI, LLM, and RAG solutions while collaborating with data scientists, engineers, and product stakeholders. Drives technical standards, design reviews, code quality, and continuous engineering improvement.

Machine Learning Engineer Interns will develop, deploy, and operate production-scale ML models and pipelines. They will lead an end-to-end research-to-production project, apply modern ML techniques to blockchain and crypto use cases, collaborate with senior engineers and product teams, and present findings to stakeholders. The role requires doctoral-level machine learning research, model development experience with PyTorch or TensorFlow, production-quality Python skills, and software engineering fundamentals.

Lead design, development, deployment, and maintenance of responsible AI/ML solutions (including LLMs, generative models, chatbots, and agent orchestration) for provider-facing products. Provide technical leadership, mentor engineers, align solutions with enterprise AI strategy and compliance, integrate AI into cloud architectures, and ensure production reliability and operational excellence.

Build and ship computer-vision models end-to-end: dataset curation, training, deployment, and monitoring across cloud and roadside edge hardware. Optimize for high-volume inference, accuracy, efficiency, and real-world robustness while collaborating cross-functionally and iterating rapidly.

Build and ship production machine learning and computer vision systems processing 11 million daily images. Own the full lifecycle from sensor data collection, dataset curation, training, and deployment through monitoring and iteration. Develop detection, segmentation, classification, embedding, and re-identification models for cloud and roadside edge hardware, balancing accuracy, speed, and power efficiency. Apply both classical computer vision and state-of-the-art methods while writing production-quality software.