Top Machine Learning Engineer Jobs

Designs, operates, and optimizes production ML inference platforms for LLMs, vision models, and recommendation systems. Responsibilities include request routing, continuous batching, caching, autoscaling, GPU and memory optimization, observability, deployment automation, security controls, incident response, and reliability improvements. The engineer collaborates with ML and product teams to launch model capabilities while balancing latency, throughput, cost, and quality.

Lead end-to-end development of an AI-driven decision-support system for multi-sensor data. Responsibilities include building data ingestion and fusion pipelines, training and deploying supervised and unsupervised ML models, optimizing near-real-time inference, creating GPU-accelerated dashboards, integrating modular containerized systems, and supporting secure deployment and testing. The role works directly with the founder, owns technical delivery autonomously, and collaborates with subcontractors on human factors, model validation, and security auditing.

Optimize large-scale AI training and inference systems for throughput, latency, memory efficiency, and cost. Responsibilities include profiling pipelines, tuning distributed training, implementing quantization and sparsity, optimizing LLM serving, developing compiler and custom-kernel improvements, building benchmarks, evaluating hardware and software, and documenting best practices. The engineer collaborates across ML and platform teams, mentors junior engineers, and translates AI systems research into production performance gains.

Design, evaluate, and deploy advanced machine learning systems across language, vision, recommendation, and structured data domains. Translate business problems into measurable ML solutions, conduct rigorous experimentation, build scalable training and inference pipelines, and optimize models for accuracy, latency, throughput, and cost. Develop data-quality and monitoring tools, implement responsible AI safeguards, collaborate across technical and product teams, document research, mentor engineers, and influence AI strategy.

Design, build, and operate reliable, high-performance inference platforms for large machine learning models in production. Responsibilities include request routing, batching, caching, autoscaling, GPU utilization, observability, distributed systems, performance engineering, capacity planning, and incident response. The role requires balancing latency, throughput, cost, and quality across diverse ML workloads, with preferred experience in globally distributed AI serving, model optimization, FinOps, open-source infrastructure, and external-facing AI APIs.

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.

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.

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