Top Machine Learning Engineer Jobs

Train and iterate custom ML models on manufacturing and design data to estimate cost and complexity, generate designs, and extract structured data from engineering documents. Build end-to-end pipelines from data cleaning and labeling through training, evaluation, and deployment. Collaborate with engineering and manufacturing teams to source datasets and identify opportunities to improve workflows with custom models.

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.

Design, build, and deploy production-ready intelligent solutions using machine learning, deep learning, generative AI, and large language models. Develop RAG pipelines, integrate AI services, manage model deployment and MLOps workflows, and apply strong software engineering practices. Collaborate with engineering, product, and data teams in a distributed environment to create scalable, reliable, responsible AI-powered products.

Build and deploy AI-powered applications for customers, combining Python and TypeScript application engineering with LLMs, generative AI, agent orchestration, APIs, distributed systems, and cloud platforms. Partner with stakeholders to understand requirements, design scalable architectures, rapidly prototype solutions, integrate AI capabilities, and evolve proofs of concept into production systems. The role also involves customer-facing consulting, technical decision-making, and collaboration throughout the solution lifecycle.

As a Senior Machine Learning Engineer, you will design and implement systems for ML and NLP, optimizing models and tools for Clover's healthcare solutions.

Develop and maintain high-performance C++ machine control and data analysis software for advanced mask inspection systems. Integrate AI/ML capabilities, collaborate with multidisciplinary engineering teams, optimize and scale existing code, and support integration/testing and customer escalations. Work with APIs, containers, and observability tools to enable reliable production deployment.

Create end-to-end computer-use tasks for AI agents in Linux desktop environments. Design realistic multi-application scenarios, prepare files and instructions, write Python setup and automated scoring scripts, test tasks, and document them. The contract requires daily check-ins, rapid feedback, and availability throughout a three- to four-week engagement. Experience with AI tools, AI/ML projects, Linux VMs, Git, reinforcement learning environments, or computer-use benchmarks is relevant.

Own end-to-end ML lifecycle for pricing and underwriting: improve model accuracy and coverage, reduce infrastructure cost, deploy and monitor production models on AWS, run experiments/backtests, and collaborate with domain experts to inform model improvements and product APIs.

Design, train, deploy, and maintain machine learning models and pipelines for EO/IR image processing and computer vision applications. Build deep learning systems for object detection, segmentation, tracking, and video analysis; collaborate with AI and engineering teams; prototype solutions, troubleshoot systems, and evaluate emerging ML techniques for operational space systems.

Develop, train, validate, deploy, and maintain deep learning models and machine learning pipelines for electro-optical and infrared image processing. Responsibilities include data ingestion, feature engineering, computer vision, rapid prototyping, operational system integration, troubleshooting, and collaboration with AI and engineering teams. The role supports defense and intelligence space programs and requires U.S. citizenship and the ability to obtain and maintain a government security clearance.

Develop and deploy machine learning models and pipelines for electro-optical and infrared image processing applications. Responsibilities include designing, training, validating, and maintaining deep learning models; building data ingestion, feature engineering, training, and inference pipelines; prototyping solutions for operational systems; troubleshooting software; collaborating on AI and computer vision projects; and evaluating emerging ML tools and techniques.

Develop and deploy deep learning models for electro-optical and infrared image processing supporting space-based defense and intelligence missions. Responsibilities include building ML pipelines for data ingestion, feature engineering, training, and inference; collaborating on computer vision and AI solutions; prototyping, integrating, troubleshooting, and maintaining operational systems; and evaluating emerging ML research, tools, and frameworks.

Designs, trains, deploys, and maintains machine learning models and pipelines for electro-optical and infrared image processing. Develops deep learning solutions for computer vision, including object detection, segmentation, and tracking. Collaborates with AI and engineering teams, prototypes solutions for operational systems, troubleshoots software, and evaluates emerging ML tools and techniques.

Develop and deploy deep learning models for electro-optical and infrared image processing supporting space-based defense and intelligence missions. Build ML pipelines for data ingestion, feature engineering, training, and inference; collaborate on AI, machine learning, and computer vision solutions; prototype systems for operational integration; troubleshoot and maintain software; and evaluate emerging ML tools and techniques.

Build and operate production machine learning systems for Airbnb Payments, including LLM agents, real-time fraud detection, anomaly detection, personalization, and checkout optimization. Design, train, deploy, monitor, and continuously improve batch and streaming AI applications at global scale. Partner with product, engineering, operations, and data science teams, establish model governance and testing standards, and mentor engineers.

Develop and deploy models to understand technical drawings and specifications, perform multimodal reasoning across diagrams, text, and structured data, translate research into production within ~1 year, present prior research, and collaborate closely with founders to deliver customer-impacting applied research.

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.