Top Machine Learning Engineer Jobs

The Staff Software Engineer will lead the development of cloud-side ML infrastructure, focusing on real-time computer vision and LLM applications, while mentoring team members and improving operational reliability.

Engineer will leverage AI for pre-silicon verification, collaborate with teams, develop AI solutions, and engage customers regarding verification strategies.

The engineer will leverage AI to enhance pre-silicon verification processes, collaborate with teams, and improve methodologies using advanced tools.

As a Senior Machine Learning Engineer, you will build and operate scalable ML systems, oversee projects from inception to deployment, and improve system performance while collaborating with cross-functional teams and mentoring junior engineers.

As a Senior Machine Learning Engineer, you will design and operate scalable ML systems for real-time personalization, collaborating with teams and mentoring engineers.

As a Research Engineer, you will collaborate on advancing AI systems through reinforcement learning, while contributing to both research and engineering tasks.

The role involves developing large-scale data processing pipelines, automating data selection, and collaborating on ML model improvements to enhance vehicle performance and safety.

The role involves deep troubleshooting, root cause analysis, and architectural optimization in the Data and AI ecosystem to enhance platform reliability and supportability.

Build infrastructure for AI/ML environments, collaborate with teams for improvements, and interact with customers to enhance product features.

As a Senior ML Infrastructure Engineer, you will design scalable architectures for ML, build robust data pipelines, manage GPU clusters, and drive improvements in CI/CD workflows while collaborating with cross-functional teams.

Build and productionize LLM-powered tools, RAG systems, and agentic workflows. Deploy and operate AI infrastructure on Kubernetes, manage model serving and vector stores, integrate identity and access controls, automate CI/CD and data pipelines, and establish evaluation and observability for deployed services in a defense-oriented environment.

Design, develop, and test AI/ML and optimization software for energy research on HPC systems. Build and evaluate models (deep learning, probabilistic methods, large-scale inference), productionize solutions, collaborate with researchers, mentor juniors, author technical publications, and support multiple projects across the research-to-implementation stack.

Lead a team building large-scale ML model evaluation and orchestration frameworks for autonomous vehicles. Own data analysis workflows, dataset creation, statistical model evaluation, regression reporting, and tool development (AWS Kubernetes orchestration, ReSimulation). Provide technical leadership, mentorship, and manage project timelines and deliverables.

Architect, build, and operate cloud and on‑prem AI/ML training and inference platforms (including federated learning). Partner with scientists and external collaborators, benchmark and optimize ML infrastructure, ensure security/compliance and cost effectiveness, mentor engineers, and represent the Institute in vendor and cross‑institutional technical engagements.

Design, build, and deploy production ML models (time series forecasting and NLP). Perform EDA, feature engineering, model evaluation, and translate business problems into ML solutions. Maintain production model-serving pipelines and collaborate across teams to deliver scalable data-driven systems.

Lead the design and delivery of a production-grade localization ML stack for autonomous vehicles: architect models for landmark-based localization across sensors, own data and evaluation pipelines, drive feature development to deployment, define failure modes and safety coverage, and mentor engineers while integrating cutting-edge multimodal and foundation-model techniques into robust, real-time localization systems.

Build end-to-end ML software for autonomy foundation models: data pipelines, training/evaluation workflows, model implementation (LLM/VLM/VLA), simulator integration, and low-latency serving and inference tooling for vehicle deployment.

Design, train, deploy, and monitor ML/AI models and MLOps pipelines (including NLP, LLM, RAG, generative AI). Optimize performance, integrate models into applications/APIs, ensure security, documentation, and collaboration with engineers, analysts, and SMEs for federal missions.

Design and operate scalable infrastructure and automation for real-time video ML workloads. Build Kubernetes orchestration, CI/CD pipelines, IaC, monitoring, and high-performance Linux/networking systems for cloud and edge deployments, collaborating with ML engineers to validate and scale model deployments.

Develop and apply AI/ML techniques for anomaly detection in cybersecurity, build and optimize models and tools using Python, Pandas, and C/C++, analyze and troubleshoot security events, work with embedded DoD devices, and support on-site activities with quarterly travel while maintaining an active Secret clearance.

As a Machine Learning Engineer, you'll build ML infrastructure, develop scalable data pipelines for farm images, and collaborate with farmers and agronomists to apply ML solutions.

Lead bring-up, administration, and operations of a large GPU/AI training cluster. Serve as bridge between researchers and hardware, ensuring SLURM jobs, parallel filesystems, networking, and monitoring operate reliably. Work across provisioning, storage, VPN/access, and traditional Linux sysadmin tasks; assist with physical racking and on-site datacenter needs. Collaborate closely with a small research team in Tokyo or San Francisco.

Develop, debug, and maintain AI applications and tools for large-scale data analysis. Collaborate with engineers and leadership to design solutions, create documentation, and improve developer workflows using modern LLM tools.

Design, implement, and scale ML-based solutions for search and advertising; define data collection, evaluation, and production serving strategies; analyze data to inform development; communicate cross-functionally; produce technical reports; and stay current with advances in LLMs, vision models, and generative AI.

Design and implement scalable, secure cloud-native platforms and services for real-time network data processing, generative AI and agentic systems. Lead architecture, mentor engineers, build production-grade distributed systems for analytics, telemetry, and intelligent automation.