Top Machine Learning Engineer Jobs

Build and operate large-scale machine learning inference infrastructure for recommendation models, embedding-generation models, and self-hosted LLMs. Responsibilities include model optimization, export, serving, hosting, LLM fine-tuning, reinforcement learning infrastructure, and inference performance improvements. The role supports highly available ML systems across Snap’s advertising, content, growth, camera, and other product teams.

Build and deploy production machine learning systems, including LLMs, agents, and RAG applications, for healthcare, government, energy, and other regulated industries. Responsibilities include developing scalable data pipelines, optimizing model performance, monitoring deployed models, translating research into practical solutions, collaborating with leaders and cross-functional teams, and contributing to software engineering practices. The role is designed for a 2027 bachelor's graduate with hands-on generative AI and machine learning framework experience.

Build and maintain AI foundations and production model services, including fine-tuning and deploying open-source models in GPU-backed infrastructure. Develop computer vision, image understanding, OCR, document layout analysis, and multimodal solutions for PDFs, images, and scanned documents. Optimize inference performance, resource utilization, throughput, and latency while debugging production systems and improving engineering standards.

Develop and deploy computer vision machine learning models for customer applications. Responsibilities include data preparation, model training and fine-tuning, computer vision algorithm development, model optimization for edge hardware, evaluation, production monitoring, drift diagnosis, and retraining. The role involves collaborating with customers, product teams, and solutions engineers, supporting on-site deployments, and contributing to shared ML tooling.

Design, develop, test, and deploy AI/ML services and agentic workflows for rugged tactical networking and computing systems. Translate business needs into technical requirements, collaborate with technical owners, and build scalable applications using Python, Linux, cloud-native infrastructure, AI frameworks, databases, observability tools, and CI/CD pipelines. The role supports mission-critical government, defense, and intelligence applications.

Designs scalable backend services, distributed systems, event-driven data pipelines, and production machine learning models. Responsibilities span feature engineering, model training, deployment, monitoring, real-time decision engines, microservices, cloud platforms, and AI/LLM workflow orchestration. The role requires collaboration across engineering, data, and product teams and is performed on-site in Manchester, New Hampshire, with no relocation available.

Leads the architecture and technical execution of Cisco’s distributed AI platform. Designs high-concurrency Go and Python microservices, agentic AI orchestration, identity and multi-tenant systems, API gateways, and secure service communication. Authors technical specifications, drives zero-trust security, CVE remediation, production triage, and platform reliability. Mentors senior engineers and establishes engineering standards across teams.

Technical Lead responsible for architecting, deploying, operating, and sustaining secure agentic AI, LLM inference, data, and platform capabilities across classified and unclassified environments. Responsibilities include JWICS hosting, cross-domain data flows, cloud architecture, observability, security hardening, integration and performance testing, troubleshooting, documentation, accreditation support, and technical leadership. The role is onsite at the Pentagon and requires routine domestic and international travel.

Own the end-to-end computer vision platform for fine-grained item identification and pricing. Responsibilities include designing and fine-tuning vision models, defining evaluation and confidence standards, building labeling feedback loops, prioritizing coverage versus accuracy, and deploying reliable, low-latency serving infrastructure at scale. The role also communicates model capabilities and trade-offs to technical and non-technical stakeholders.

Designs, builds, and operates production-grade agentic AI systems at enterprise scale. Responsibilities include multi-agent orchestration, tool use, planning, memory, retrieval-augmented generation, hybrid search, model integration, evaluation, observability, safety guardrails, and governance. The role provides architectural leadership, mentors engineers, leads design reviews, and develops scalable AI infrastructure across hyperscalers and company platforms.

Designs, develops, deploys, and optimizes generative AI and machine learning solutions on AWS. Builds Amazon Bedrock and RAG applications, document-processing bots, APIs, and scalable ML models. Responsibilities include model evaluation, monitoring, troubleshooting, continuous improvement, data extraction, and cross-functional AI integration. The role requires expertise in Python, cloud architecture, OCR technologies, NLP, deep learning, CI/CD, and web application development.

Supports machine learning development for fraud detection, including data preprocessing, feature engineering, model training, evaluation, experiment tracking, drift monitoring, and batch scoring. Maintains Python data pipelines, Jupyter reporting artifacts, and MLflow documentation. Assists with NLP/NER transformations and UiPath Maestro integration testing while following government security, data governance, CI/CD, and EPLC documentation requirements.

Lead end-to-end machine learning solutions for League of Legends, from problem framing to production. Build personalization, recommender, matchmaking, and in-game ML systems; design evaluations and experiments; integrate models with game and service engineering; create reusable ML patterns; mentor engineers and improve ML observability, monitoring, and launch practices.

Lead the design, development, optimization, and production deployment of advanced machine learning models and algorithms. Build scalable ML pipelines, preprocess and analyze large datasets, ensure data quality, monitor model performance, and collaborate with data scientists, software engineers, and product teams to integrate AI/ML solutions into products and services.

Lead the architecture and production operation of Grubhub’s ranking, recommendation, retrieval, and query-understanding systems. Drive improvements in long-term diner value through deep learning, embeddings, LLMs, experimentation, and real-time inference. Establish model evaluation, deployment, observability, and reliability standards across teams. Partner with Product, Search, Ads, and Data Platform, mentor engineers, influence technical strategy, and communicate trade-offs to executive stakeholders.

Own the architecture and technical direction of large-scale ranking, recommendation, retrieval, and query-understanding systems. Lead production machine learning across deep learning, embeddings, LLMs, experimentation, model deployment, and observability. Partner across Product, Search, Ads, and Data Platform, mentor engineers, drive architecture reviews, improve operational standards, and translate technical trade-offs for executive stakeholders.

Develop, train, evaluate, debug, and improve machine learning models for real-world applications. Own the ML lifecycle from dataset construction and experimentation through deployment, while applying deep learning, reinforcement learning, and LLM techniques. Collaborate with engineering and product teams, contribute to scalable ML infrastructure and tooling, define evaluation metrics, analyze errors, and translate research concepts into practical solutions.

Develop ML-powered motion planning systems for Level 4 autonomous vehicles. Responsibilities include building learned planners and end-to-end models, applying imitation and reinforcement learning, integrating ML with rule-based and optimization-based planning, training and evaluating models using real-world data and simulation, and deploying production systems under safety, latency, and onboard compute constraints. The role also involves scenario mining, evaluation infrastructure, closed-loop simulation, and collaboration across autonomous driving teams.

Lead camera ISP tuning and image quality evaluation for autonomous vehicle cameras. Collaborate with cross-functional teams and vendors to define specs, characterize sensors, develop ISP configurations, run objective/subjective IQ tests (Imatest), and optimize ISP for perception performance in production vehicles.

Build and operate MLOps infrastructure for production AI agents, including evaluation frameworks, CI/CD quality gates, model access across cloud providers, experiment tracking, observability, versioned deployments, and cost optimization. Develop and fine-tune LLMs and machine learning models, monitor production quality and drift, and collaborate with AI engineers and clinical experts to deliver reliable healthcare claims automation.

Own the system that enables AI characters to remember users across hundreds of conversations, including memory schema design, information extraction, and compression. Develop and evaluate improvements based on user retention and recall accuracy. Work with a small, fast-moving engineering team in person at the Palo Alto office, taking ownership of ambiguous problems and defining effective solutions.

Design, build, and deploy scalable AI/ML models and pipelines for consumer healthcare. Develop production code (Python/Java/Scala), automate infrastructure (Terraform, Kubernetes), validate model performance, integrate cloud services (Azure/AWS), and collaborate cross-functionally to monitor and document enterprise-scale AI solutions.

Develop, train, evaluate, and deploy machine learning models using Python and PyTorch for defense applications. Build backend software, data pipelines, APIs, and system integrations supporting ML workflows. Prepare datasets, implement published ML methods, work in Linux-based containerized environments, and collaborate across project teams. Document and communicate technical results while contributing to development processes and operationally relevant prototypes.

Design, build, and deploy scalable ML pipelines for multimodal neuroscience data. Research, train, and deploy advanced deep learning and reinforcement learning models to predict brain states, detect pathological patterns, and generate personalized neuromodulation parameters. Select AI frameworks and cloud infrastructure, collaborate with computational neuroscientists, and contribute to IP and publications.

Design, build, and deploy autonomous AI agents using LLMs and agent frameworks; integrate agents into e-commerce and supply chain systems; implement multi-agent orchestration, memory and tool integrations; fine-tune models and apply RL concepts; and build safety, monitoring, and MLOps pipelines.