Top Machine Learning Engineer Jobs

Architect, deploy, operate, and monitor on-premises LLM infrastructure and agentic workflows. Build RAG pipelines, MCP servers, data pipelines, CI/CD integrations, testing environments, tracing and evaluation tools, and model-serving network requirements. Support Kubernetes-based AI/ML infrastructure and software development environments while collaborating with cross-functional teams. The role requires a bachelor's degree with four years of related experience or a master's degree with two years, plus TS/SCI clearance.

Leads architecture, deployment, monitoring, automation, governance, and lifecycle management for production machine learning systems. Manages datasets, ML pipelines, model registries, evaluation, drift monitoring, testing, rollback, security, compliance, and audit documentation. Partners with data, cloud, software, cybersecurity, testing, operations, and government stakeholders to deliver secure, traceable, mission-critical ML capabilities.

Build and operate production machine learning capabilities, including dataset management, automated training and deployment pipelines, model monitoring, drift detection, governance, testing, rollback, and secure cloud integrations. Collaborate with data, software, cloud, cybersecurity, testing, operations, and government teams. Maintain traceability, documentation, auditability, incident response, and human-review controls across the model lifecycle.

Lead the design, development, productionization, and monitoring of machine learning models and pricing algorithms for Uber’s large-scale, real-time marketplace. Develop optimization solutions using deep learning, causal modeling, reinforcement learning, and algorithmic game theory. Set technical direction, mentor engineers, collaborate cross-functionally, and deliver scalable systems serving billions of trips and over one million predictions per second.

Develop, deploy, and support secure, scalable production software and AI/ML solutions. Collaborate with product, UX, and engineering to deliver features, create tests and automation, instrument monitoring and dashboards, and participate in agile processes while continuously learning and improving team practices.

Develop and scale AI/ML platform services supporting model training, serving, and management. Build microservices and paved-path solutions for data scientists and ML engineers, evaluate platform capabilities, and support personalization, recommendation, search, and other machine learning use cases. Collaborate with engineering, data science, product, and agent development teams while contributing to architecture, technical design, code reviews, and improved development practices.

Build scalable infrastructure for LLM-based agentic AI supporting autonomous security operations. Responsibilities include designing agent orchestration frameworks, LLM inference and resource-management systems, MCP servers, supporting APIs, and distributed backend services. The role collaborates with machine learning, security research, engineering, and product teams. Required experience includes backend infrastructure, distributed systems, APIs, microservices, cloud deployment, containerization, orchestration, and Python, Java, or Go.

Develop hybrid physics-and-data scientific machine learning models and LLM-based agentic workflows for computational modeling and simulation. Build, validate, deploy, and support workflows using real simulation solvers, HPC or cloud infrastructure, provenance tracking, reproducibility controls, evaluation harnesses, and human-in-the-loop safeguards. Collaborate with drug product, device, process, manufacturing, quality, regulatory, and digital technology teams to deliver reliable tools for pharmaceutical and medical-device development.

Build and own a scalable API platform that enables external organizations to upload data, fine-tune and evaluate AI models, and run low-latency inference for robotics applications. Responsibilities include designing multi-tenant infrastructure, APIs, deployment integrations, observability, reliability, and developer tooling. The role requires strong backend and systems engineering skills, Python expertise, production machine learning familiarity, and end-to-end ownership of ambiguous platform problems.

Develop and optimize CUDA kernels and machine learning software for training and inference on advanced GPUs. Profile workloads, improve GPU utilization, build benchmarking and automation tools, troubleshoot system bottlenecks, support new model architectures, validate numerical and gradient correctness, review code, and contribute to technical documentation and industry events.

Develop and scale distributed machine learning infrastructure for large-scale LLM pre-training. Responsibilities include extending frameworks such as DeepSpeed and FSDP, implementing distributed optimizers, managing multi-node GPU launches, building experiment tracking and monitoring, improving reliability and performance, and maintaining data-loading and checkpointing workflows. The role requires strong Python, systems engineering, distributed training, multi-node debugging, and large-scale pre-training experience.

Build and scale end-to-end reinforcement learning infrastructure for foundation models. Responsibilities include extending distributed training frameworks, integrating rollout and inference systems, implementing reward and trajectory processing, configuring multi-node GPU launches, monitoring experiments, optimizing performance, and developing checkpointing and failure-recovery systems. The role requires strong Python and ML systems engineering experience, hands-on LLM RL pipeline ownership, and expertise with distributed training and policy optimization methods.

Own the applied AI layer of a software platform by building production AI services, retrieval systems, structured extraction pipelines, evaluation suites, guardrails, and agentic workflows. Manage model performance, reliability, latency, cost, and monitoring. Translate product goals and user feedback into measurable experiments and shipped improvements while leading complex projects and maintaining dependable live systems.

Develop and optimize large-scale machine learning models, open-source LLM infrastructure, and training and inference systems for advertising applications. Build distributed data and model pipelines, adapt DeepSpeed and OpenRLHF, optimize GPU/TPU serving for latency and cost, monitor model performance, and collaborate with applied scientists on research. The role focuses on recommender systems, personalization, targeting, reinforcement learning, and ad intelligence at massive scale.

Support a government customer as an expert AI/ML Engineer by developing advanced AI/ML architectures, responsible AI implementations, enterprise deployment strategies, and roadmaps advancing technologies from research to operational deployment. The role requires expertise in neural networks, reinforcement learning, model interpretability, MLOps pipelines, productionizing data science research, and deploying GPU-enabled deep learning applications.

Develop, train, deploy, and optimize AI/ML models for object detection, data triage, search, inference, automated discovery, and decision-making. Manage model versioning systems, contribute to AI/ML capability development, and identify model vulnerabilities while supporting a government customer.

Own Arcade’s end-to-end machine learning lifecycle, including training pipelines, model development, evaluation, production telemetry, and releases. Build compact, efficient models for tool selection, routing, retrieval, recommendations, and agent memory. Productionize models for customer VPCs and air-gapped environments through quantization, optimization, packaging, and serving. Set the ML strategy and stack, establish evaluation practices, convert agent traces into training data, and help grow the ML team.

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.

Designs, builds, and deploys production-grade AI and machine learning systems for national security missions. Responsibilities span data processing, model development, MLOps, APIs, microservices, distributed systems, and operational integration. The role focuses on NLP, LLMs, generative AI, computer vision, hybrid interpretable models, and robust deployment across synthetic and real-world sensing environments.

As an ML Engineer - Robotics, you'll design, train, and deploy intelligent models for autonomous systems, focusing on perception, planning, and decision-making pipelines, while collaborating with robotics engineers to evaluate model performance.

As a Founding ML Engineer, you'll develop ML models for lead optimization, automate demand generation processes, build data pipelines, and collaborate with teams to enhance growth through AI solutions.

Operate and improve enterprise AI/ML platforms across Palantir Foundry, AWS Bedrock, SageMaker, and cloud-native services. Responsibilities include MLOps automation, deployment, monitoring, observability, governance, incident response, root cause analysis, reliability engineering, and lifecycle management for production AI workloads. The role also provides technical leadership, mentorship, and cross-functional collaboration while supporting Generative AI, LLM, and RAG solutions.

Build and deploy production-grade agentic AI systems that automate enterprise workflows and support decision-making. Responsibilities include developing autonomous and semi-autonomous agents, benchmarking model performance, designing scalable AI/ML systems, conducting experiments, and improving evaluation and monitoring. The role requires collaboration with engineering and product partners, alignment with security and compliance standards, and application of software engineering, cloud, DevOps, machine learning, and MLOps practices.

Build and operate the machine learning inference stack for SambaNova’s RDU accelerators. Develop production Python features including speculative and constrained decoding, tool calling, prompt caching, long-context inference, and vLLM integration. Maintain public inference APIs, create accuracy and regression tests, and troubleshoot latency, throughput, and reliability issues. The role requires strong systems and programming fundamentals, experience with LLM serving frameworks and inference techniques, and PyTorch knowledge.

Build and operate production machine-learning systems, including training, deployment, monitoring, retraining, APIs, data pipelines, dashboards, and orchestration. Develop customer-facing workflows for model refreshes and health visibility, automate validation and rollout processes, and improve Kubernetes, CI/CD, observability, and deployment tooling. Partner with customers and internal teams to turn tailored solutions into reusable platform capabilities while ensuring reliability, rollback safety, and operational maintainability.