Top Machine Learning Engineer Jobs

Lead design and delivery of LLM- and agent-driven products end-to-end: build distributed agentic systems, retrieval and grounding pipelines, production inference stacks, evals and labeled-data flywheels, and partner cross-functionally to harden ML into reliable, mission-critical services.

Lead design, build, deploy, and maintain production ML and AI systems (including LLM inference, RAG, similarity search, observability, and governance). Collaborate cross-functionally to create data pipelines, retrain and monitor models, and apply responsible, explainable AI practices while contributing technical vision and roadmap for AI solutions.

Lead development, deployment, and maintenance of production ML and AI systems (including LLM inference, RAG, similarity search, observability, and governance). Collaborate cross-functionally to build data pipelines, fine-tune foundation models, apply responsible AI practices, and contribute technical vision for scalable, high-performance AI infrastructure in risk management.

The AI/Machine Learning Engineer will design, develop, and deploy AI models for healthcare, collaborating with teams to enhance patient outcomes using advanced data analytics and machine learning techniques.

Lead development and optimization of LLMs and Mixture-of-Experts models, drive research for performance and efficiency (quantization, on-device inference, RAG, agents), integrate models into product, mentor engineers, and deliver cross-functional initiatives.

Develop and implement state-of-the-art synthetic sensor simulation methods (photorealistic images, lidar, radar). Train and evaluate ML models for synthetic data, analyze realism and utility, answer sensor-autonomy questions, and collaborate across autonomy, infrastructure, and systems teams. Role scoped as Senior/Staff IC with potential to grow into Tech Lead.

Develop and deploy automation and ML integration solutions for government incident response: build data pipelines, malware automation/detonation workflows, AI/LLM integrations, API integrations, feature engineering automation, monitoring, and scale pilot systems to production while ensuring data quality and documentation.

Design and implement enterprise-scale AI/ML architectures and solutions, build and maintain data and model pipelines, process structured and unstructured data, deploy and monitor ML applications, mentor teammates, and operate with autonomy while meeting security clearance requirements.

Design, build, deploy, and maintain production ML/GenAI systems focused on NLP tasks (summarization, semantic search, classification, prediction). Collaborate cross-functionally to operationalize models, build scalable data pipelines, ensure model monitoring/compliance, and mentor junior engineers while contributing to architecture and best practices.

Build core, shared ML capabilities for a production platform: foundation models for documents, extraction agents, model routing, unified evaluation, and continuous improvement pipelines. Own end-to-end capability development, production deployment, and cross-team support to improve accuracy, latency, cost, and reliability across institutional workflows.

As a Machine Learning Engineer, you'll build and enhance features for a search stack, design ML/NLP solutions, and implement an experimentation framework while ensuring code quality.

Develop and deploy machine learning models for national security challenges. Responsibilities include algorithm development, model training, deployment, and collaboration with teams for effective ML solutions.

Build, train, fine-tune, evaluate, and deploy machine learning and LLM models for production. Create monitoring, evaluation, and feedback tooling, evaluate new techniques, and collaborate cross-functionally to ship ML-powered features.

Build and deploy agentic, full-stack AI systems using LLMs, embeddings, RAG, and agent orchestration for defense and intelligence customers. Collaborate with customers, operate in classified/air-gapped environments, codify reusable patterns, and support end-to-end delivery from prototype to production.

The Founding ML Engineer will develop and deploy ML systems, collaborate with teams, and translate ambiguous problems into production ML solutions, shifting between client delivery and platform development.

As a Machine Learning Engineer, design imaging algorithms, build acoustic simulations, and develop computer vision systems to enhance brain imaging technology.

The role involves training and fine-tuning vision and language models, building evaluation and learning pipelines, and optimizing model performance.

Develop and implement automation for ML technology integration, including data pipelines, AI/LLM integrations (Databricks, Bedrock), malware analysis workflows, feature engineering automation, API integrations, monitoring, and scaling automation from pilot to production while collaborating with analysts and maintaining documentation.

The Machine Learning Engineer will design and build scalable ML data pipelines, manipulate 3D data, collaborate with customers, and ensure reliable deployment of ML systems.

The Applied ML Engineer will research and build AI/ML systems, own model development, partner with teams to impact marketplace metrics, and establish best practices for AI/ML at Paraform.

Design and implement AI agents for mission planning and orchestration across autonomous systems, focusing on LLM frameworks and edge computing.

The Senior Staff Machine Learning Engineer will improve Coupang's search and discovery systems using advanced algorithms, addressing bottlenecks, proposing solutions, and leading projects involving personalization and recommendations.

As a ML Engineer at Apiphany, you will develop advanced machine learning models, focusing on engineering data, AI systems, and real-world performance.

Design and implement 3D machine learning models, process reality capture data, and integrate AI solutions in production environments for robotics applications.

The Staff Machine Learning Engineer will lead AI initiatives, architect LLM systems, design evaluation frameworks, and mentor engineers.