Top Machine Learning Engineer Jobs

Design and integrate production AI/ML systems for USCYBERCOM, including LLM agents, orchestration workflows, model routing, RAG pipelines, vector databases, MCP tool integrations, evaluation frameworks, and governance guardrails. Partner with data scientists and mission analysts to transition prototypes into secure, enterprise-scale capabilities while evaluating emerging AI technologies and managing operational risks.

Own the design, provisioning, operation, and optimization of cloud-based GPU clusters supporting large-scale model training and inference. Partner with AI engineers to improve training performance and hardware utilization, contribute to ML libraries and infrastructure tooling, develop GPU compute strategies, optimize multi-cloud capacity and costs, and improve reliability, testing, documentation, and engineering practices.

Develop high-performance software for silicon test pattern generation, silicon application, failure analysis, and yield learning. Create parallel graph traversal and analysis methods, collaborate across technical teams, and apply LLMs, RAG, graph-based machine learning, and reinforcement learning to electronic design automation solutions. The role also involves large-scale model training, data processing, agentic applications, and advanced multi-agent and vector database systems.

Design and deliver production-quality Metropolis AI solutions using Cosmos foundation models. Identify model limitations, generate synthetic data, refine tuning and architectures, partner with Cosmos and cross-functional teams, open-source research artifacts, and advance generative and simulation-based perception for physical AI deployments.

Design and productionize machine learning solutions that predict and optimize GPU-accelerated Apache Spark workloads. Develop adaptive ML systems, AI agents, and optimization tools; collaborate with customers and partners on complex deployments; track advances in ML systems; and mentor engineers in data science and machine learning. The role requires expertise in large-scale data processing, modern ML/DL methods, LLMs, generative AI, reinforcement learning, online learning, feature engineering, and boosted-tree models.

Develop and productize deep learning perception systems for autonomous driving, including traffic signal, sign, road marking, and construction object understanding. Lead end-to-end model development, data-driven improvement, simulation, augmentation, and deployment to embedded platforms. Optimize solutions for accuracy, safety, latency, robustness, and regional generalization while collaborating with data-labeling teams and contributing technical leadership.

Develop and ship a meaningful engineering project, monitor its production deployment, collaborate with a mentor and manager, test and document work, and present results to the engineering organization. The role may involve machine learning, backend or frontend development, APIs, data processing, deployment, and testing using technologies such as Python, Java, JavaScript, AWS, AngularJS, or React.

Own and build the company’s machine learning infrastructure, including a shared feature store, model-serving systems, drift monitoring, and unified deployment pipelines. Establish long-term MLOps standards and processes from the ground up, while collaborating with data scientists and engineers to drive adoption and improve production model reliability.

Design and lead cloud and in-car data mining strategies for autonomous vehicle perception and end-to-end models. Build AI-driven software to accelerate data mining, collection, delivery, and corner-case resolution. Develop operational processes using big data, statistics, and machine learning to ensure high-quality training data from production and collection vehicles. Collaborate with platform teams and balance cost, engineering quality, and delivery timelines.

Develop and productize machine learning models for autonomous vehicle perception, including LiDAR and camera perception, sensor fusion, detection, classification, segmentation, and tracking. Build scalable data, training, metrics, and performance workflows, and implement efficient C++ code for the NVIDIA DRIVE AV platform. Collaborate across engineering teams, track advances in machine learning, and optimize models for real-time embedded inference.

Develop and productize machine learning prediction systems for autonomous driving. Responsibilities include designing end-to-end prediction solutions, researching deep learning models, supporting diverse driving environments, prioritizing data collection and labeling, using simulation and augmentation, and meeting safety, latency, and software robustness requirements. The role also involves collaboration with data teams and deployment of real-time ML solutions to embedded platforms.

Develop GPU code generation and compiler optimizations for TensorRT to improve generative AI inference performance. Responsibilities include operator fusion, scheduling, specialization, profiling, debugging, numerical validation, and performance analysis. The role requires C++ software development and GPU architecture expertise, with collaboration across hardware, compiler, kernel, and deep learning teams. Preferred experience includes CUDA, MLIR, GPU kernels, Tensor Core optimization, inference frameworks, and low-level NVIDIA performance tools.

Analyze and optimize distributed machine-learning training and inference performance across models, frameworks, compilers, kernels, GPUs, networks, and hosts. Define fleet-wide performance metrics, diagnose regressions, evaluate automated diagnoses, build reusable analysis methods, and collaborate with partners to improve workloads and product capabilities. The role requires strong programming, debugging, statistical judgment, and communication skills, with proficiency in Python and CUDA.

Develop and deploy AI infrastructure solutions across NVIDIA cloud, partner, and hyperscale environments. Build distributed training, inference, and MLOps pipelines; manage Kubernetes-based GPU workloads; optimize performance, cost, and reliability; and implement observability and SLO/SLA monitoring. Provide hands-on technical support to strategic customers and partners, troubleshoot production issues, integrate enterprise systems, and create implementation guides, runbooks, and postmortems.

Lead development of large-scale reinforcement learning frameworks and simulation pipelines for autonomous vehicle foundation models. Design closed-loop simulation, optimize reward functions and simulation quality, build scalable data-processing workflows, and improve training reliability on GPU clusters. Develop monitoring and debugging tools while collaborating with platform, product, and research teams to integrate advanced model architectures and productize machine learning solutions.

Develop algorithms and optimizations for inference and compiler stack, enhance performance metrics, collaborate with hardware teams, and publish work on novel approaches.

Lead the design and optimization of GPU-cluster infrastructure supporting AI and ML research. Partner with researchers, data engineers, and DevOps teams to identify bottlenecks, improve researcher efficiency, and build scalable, highly available systems. Drive infrastructure roadmaps, optimize resource utilization, define performance metrics, and oversee distributed training operations across HPC environments. The role requires deep expertise in accelerated computing, storage, scheduling, networking, containers, cloud platforms, and AI/ML frameworks.

Lead the design and delivery of production machine learning systems for infrastructure observability, troubleshooting, and optimization. Develop anomaly detection, time-series analysis, event correlation, recommendations, ranking, and root-cause inference capabilities. Build evaluation frameworks, data pipelines, feature workflows, model-serving services, monitoring, and feedback loops. Integrate ML with telemetry platforms and customer-facing products, establish engineering standards, mentor engineers, and influence technical roadmaps across teams.

Develop and evaluate machine learning models for object classification, anomaly detection, and data-driven space mission tasks. Support data ingestion, preprocessing, feature engineering, experimentation, and model evaluation. Write clean, tested Python code while collaborating with senior engineers. The role is fully onsite in Denver or Long Beach and requires compliance with U.S. ITAR technology export regulations.

Develop, train, and evaluate machine learning models for space-domain applications including object classification and anomaly detection. Build data ingestion, preprocessing, feature engineering, and model evaluation pipelines; conduct experiments; track results; and write clean, testable Python code. Collaborate with senior engineers while contributing to AI capabilities supporting space security and autonomous systems.

Own machine learning models end to end, from data preparation and training through evaluation and production deployment. Build pipelines for large multimodal datasets including video, sensor streams, and language. Collaborate with engineering and computer vision teams, rapidly prototype solutions, apply relevant research, and continuously improve production models and tooling.

Own the end-to-end machine learning and LLM lifecycle, taking models and agentic AI workflows from experimentation into reliable, scalable production. Design MLOps infrastructure, serving, orchestration, CI/CD, evaluation, observability, and GPU optimization. Build self-service tooling and establish LLMOps standards while partnering with engineering and business teams. The role is fully remote and involves up to 10% travel.

Leads the design, development, deployment, and operation of production AI/ML solutions, including generative AI, RAG, agentic workflows, conversational AI, predictive models, and automation. The role establishes responsible AI practices, integrates enterprise data and APIs, improves evaluation and observability, partners with cross-functional teams, mentors engineers, and drives initiatives from proof of concept through production scale.

Take end-to-end ownership of ML problems from framing and experimentation through deployment and support in customer environments. Build, deploy, and maintain production ML models that inform engineering decisions, and communicate technical tradeoffs to cross-functional and non-technical stakeholders.

The Machine Learning Engineer will develop and deploy AI systems for robotics, focusing on advanced manipulation techniques and control algorithms, collaborating across teams.