Top Machine Learning Engineer Jobs

Develop real-time intraoperative intelligence software for robotic surgery. Responsibilities include designing and implementing computer vision and machine learning algorithms, developing user-facing software, translating clinical requirements into technical specifications, supporting integration and testing, producing formal documentation, reviewing patents, and contributing to system architecture. The role requires expertise in computer vision, machine learning, GPU optimization, and production software development using C++ or Python.

Develop, deploy, and analyze AI/ML solutions for wired and wireless signaling data. Build software using neural-network libraries and Python, C, or C++, apply RF and digital signal processing knowledge, benchmark and optimize trained models, and solve complex problems collaboratively. The role may guide junior employees, support Agile modernization and automated testing, requires a STEM bachelor’s degree, eligibility for TS/SCI clearance with polygraph, and travel up to 25%.

Conduct advanced AI/ML research and develop novel methods, models, and mission-focused technologies. Build, train, validate, and integrate deep learning systems using modern frameworks and GPU hardware. Research areas include adversarial, trustworthy, safe, agentic, deep, and generative AI. Communicate findings through publications, technical reports, presentations, and study plans while collaborating with interdisciplinary teams and leading technical tasks.

Develop and deploy full-stack software applications using C#/.NET, Tungsten Automation, SaaS, AI/ML, OCR, and generative AI. Build intelligent document-processing workflows, classification and extraction solutions, dashboards, integrations, and custom applications connected to enterprise repositories and systems of record. Perform code migration, testing, CI/CD deployment, production support, version control, change management, and security management across an agile software lifecycle.

Lead design and build of scalable batch and streaming data pipelines for multimodal and spatial AI. Enable dataset curation, versioning, lineage, and data quality for research-to-production ML workflows. Partner with researchers and architects, run proofs‑of‑concept, optimize cost and performance, and mentor engineers to maintain observable, reproducible AI data systems.

Develops full-stack software and AI/ML solutions for federal pilot projects, including Google Gemini AI integrations. Builds APIs, works with SQL and NoSQL databases, collaborates with data science and prototyping teams, solves complex technical problems, and participates in Agile development. Requires expertise in multiple programming languages, Angular front-end development, back-end systems, and secure, scalable application delivery.

Lead design, development, and deployment of scalable AI/ML applications and MLOps pipelines on Kubernetes. Mentor engineers, define best practices, adopt emerging GenAI tools, ensure observability, security, and alignment with business objectives, and maintain documentation and runbooks.

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.

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.

Design, develop, and implement AI/ML software solutions and prototypes using full-stack technologies. Build REST APIs, integrate SQL and NoSQL databases, and collaborate with data science teams on AI/ML, big data, and business intelligence applications. Participate in Agile Scrum ceremonies, sprint delivery, testing, demonstrations, and retrospectives while solving complex technical problems in distributed teams.

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.

Designs and integrates LLM and agent orchestration workflows, model routing, RAG pipelines, vector databases, memory services, and MCP tool integrations. Evaluates and tunes models, builds automated AI evaluation pipelines, implements governance guardrails, and transitions research prototypes into production AI capabilities. The role also researches emerging AI technologies and requires collaboration with data scientists and mission analysts in a DoD mission environment.

Create, solve, review, and validate coding-heavy machine learning tasks for AI training. Build and optimize models, training and inference workflows, data pipelines, evaluation systems, and reproducible Python workflows. Diagnose numerical and system-level failures, improve memory use and throughput, and verify technical correctness while working with coding agents.

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.

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.

Build and deploy scalable AI/ML systems for retrieval, ranking, categorization, triage, and generative AI across healthcare data. Own solutions end-to-end from problem framing and model selection through production deployment and optimization. Collaborate with software engineers, product managers, clinicians, and operations teams to translate complex business problems into explainable, maintainable systems with measurable outcomes.

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.

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.

Participate in the Gradient Development Program, complete technical training, pursue Google Cloud certifications, and collaborate with technical and business leads to translate business requirements into effective AI and machine learning solutions.

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.