Top Machine Learning Engineer Jobs

Lead development of an evaluation platform for agentic AI and LLM systems. Design benchmarking, human review, LLM-as-judge, monitoring, annotation, dataset versioning, and data quality workflows. Own technical architecture, production infrastructure, stakeholder reporting, and platform health while partnering with research, product, and platform teams. Mentor engineers and support scalable AI experimentation and deployment.

Builds, secures, and operates AI development infrastructure and production systems using DevSecOps practices. Responsibilities include creating CI/CD pipelines, Infrastructure as Code, deployment automation, and reusable infrastructure modules; managing Kubernetes and Docker workloads across cloud, on-premises, and classified environments; implementing security, identity, vulnerability, supply-chain, and observability controls; and supporting AI integration across mission environments.

As a Senior ML Ops Engineer, you will bridge Data Science and Engineering to automate ML workflows, manage CI/CD for ML systems, and collaborate with various teams to implement solutions for AI features and services.

Lead the architecture, development, modernization, scaling, and reliability of enterprise software and ML platform systems. Use AI coding assistants, drive technical decisions, perform code and design reviews, establish engineering best practices, and mentor engineers. The role requires expertise in distributed systems, cloud infrastructure, production services, and high-quality software delivery, with preferred experience in microservices, containers, CI/CD, public clouds, Salesforce, or ServiceNow.

Designs and operates production multi-agent AI systems, LLM workflows, scoring and optimization algorithms, and closed-loop autonomous processes. Responsibilities include AI model evaluation, production monitoring, governance, third-party AI integration, federated data retrieval, and identifying patentable inventions. The role requires deep machine learning expertise, algorithm development, and deployment experience across complex operational environments.

Lead design and build of a scalable, multi-tenant AI/ML platform powering traditional ML, LLMs, and agentic systems. Own end-to-end architecture, production-grade backends, orchestration, LLMOps, vector DB integration, and cross-functional delivery while enforcing engineering standards, security, and observability.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

Lead applied research and engineering for feedback-driven agentic AI: define reward modeling and reinforcement learning roadmap, design production learning loops and evaluation frameworks, run large-scale experiments, and ship research into production while partnering across product and engineering.

Join the Inference team to implement features and improvements for model serving on a GPU platform, focusing on performance and reliability.

The role involves optimizing large-scale ML infrastructure for autonomous vehicles, conducting analyses, collaborating with teams, and improving system performance.

Build large-scale machine learning systems for advertising, including ranking, recommendation, targeting, prediction, optimization, and creative generation. Develop feedback loops connecting generative AI outputs to advertising performance signals, apply prompt engineering and post-training methods, run experiments, and ship reliable production ML systems. Collaborate across Product, Research, Engineering, and go-to-market teams to deliver AI-native advertising products.

Design and scale machine learning infrastructure for personalization, search, ranking, advertising technology, and real-time learning at consumer scale. Build distributed systems, data pipelines, feature infrastructure, model-serving systems, and low-latency decision systems. Lead architecture and zero-to-one initiatives, partner across product and engineering teams, mentor technical leaders, improve experimentation velocity, and explore LLM applications for personalization and conversational search.

Own the full lifecycle of detection and tracking models, including data curation, annotation, training, evaluation, optimization, and deployment to embedded edge platforms. Develop object detection and multi-object tracking systems using mission-specific sensor data, diagnose performance issues, and collaborate with systems engineers to integrate models into the Altra platform. Optimize models for SWaP-constrained environments and support electro-optical and infrared modalities.

Design, build, and optimize Python backend services, APIs, integrations, and data pipelines supporting intelligent agent workflows. Implement LLM, embeddings, RAG, and vector database solutions using AI/ML frameworks and external APIs. Improve deployed agents through prompt engineering and fine-tuning, while contributing to testing, CI/CD, monitoring, architecture, documentation, and enterprise deployment reliability.

Develop and deploy large-scale machine learning systems for advertising and recommendation, including user signals, representation learning, ranking, retrieval, prediction, and optimization. Build scalable models, training infrastructure, evaluation tools, and production systems. Improve data and feature quality, model performance, training and inference efficiency, and system reliability. Collaborate with engineering, data, and product teams to move ML innovations from experimentation into production.

Develop and optimize large-scale advertising ranking models for CTR and CVR prediction, calibration, user representations, and sequence modeling. Own machine learning systems end to end, including data pipelines, feature engineering, training, evaluation, deployment, and maintenance. Collaborate with platform, product, data science, and engineering teams to run experiments and improve advertiser outcomes, revenue, and user relevance. Apply deep learning and recommendation techniques within production latency, reliability, and cost constraints.

Integrate and optimize LLM and VLA models within Bedrock’s Rust-based onboard infrastructure for autonomous heavy machinery. Responsibilities include profiling inference performance, optimizing models with TensorRT and edge runtimes, streaming camera and LiDAR data, eliminating memory, compute, and IPC bottlenecks, and validating improvements on autonomous equipment. The role requires systems programming, GPU and CUDA knowledge, real-time systems expertise, and experience with PyTorch or JAX.

Deploy and maintain state-of-the-art ML models (LLMs and vision) in production for government customers. Improve models via retraining, tuning, and architecture updates; build scalable ML infrastructure; prototype ML-driven product enhancements; work with massive datasets and multimodal fine-tuning pipelines; collaborate across research, product, and engineering. Requires active security clearance and light travel (~10%).

Develop software for autonomous vehicles that enables safe, smooth, and predictable driving behavior. Frame complex real-world problems, build algorithms and evaluation tools, scale them across large data pipelines, and optimize them for real-time vehicle operation. Collaborate with multidisciplinary teams on motion planning, robotics, machine learning, and autonomous driving systems.

Develop and deploy computer vision, reinforcement learning, and multimodal machine learning systems for biomedical imaging and pathology research. Train and benchmark models, improve computational workflows, and support closed-loop microscopy experiments. Collaborate with biologists, imaging scientists, and engineers, contribute to manuscripts and grant proposals, and communicate technical concepts to nontechnical stakeholders. The role focuses on applying AI to neurodegeneration and other disease-related research.

Build and deploy scalable machine learning models and pipelines, integrate ML into products, and develop robust systems using Python, FastAPI, React, and AWS. Lead technical design, code and architecture reviews, agile ceremonies, incident response, and reliability practices. Mentor engineers, improve testing and CI/CD, drive cross-functional alignment, and establish high standards for software quality, documentation, observability, and maintainability.

Owns the architecture, development, evaluation, and production deployment of generative AI, agentic AI, computer vision, and reinforcement learning systems for mission-critical government applications. Responsibilities include building scalable ML infrastructure, improving models through tuning and retraining, developing evaluation benchmarks, partnering with users and research teams, mentoring engineers, and designing for classified and resource-constrained environments.

Staff-level ML and agent systems engineer responsible for production model lifecycles, multi-step agentic systems, evaluations, retrieval and context engineering, and model cost and latency optimization. The role owns technical direction for Code Understanding products, improves model and agent quality, establishes evaluation and monitoring standards, mentors engineers, collaborates with customers, and delivers reliable AI-powered developer experiences at enterprise scale.

Develop and sustain cloud software and AI/ML capabilities for national security applications. Responsibilities include requirements analysis, Java-based distributed software development, AWS cloud integration, RESTful services, testing, deployment, troubleshooting, and technical documentation. The role works with Hadoop and big-data technologies, Linux, Kubernetes, infrastructure as code, CI/CD, LLMs, RAG, and agentic AI. An active TS/SCI clearance with polygraph and at least five years of software engineering experience are required.

Performs security assessments and engineering for AI/ML systems, large-scale compute clusters, AWS Cloud infrastructure, networks, and enclave environments. Designs and integrates information assurance architectures, identifies and mitigates security risks, validates security requirements, supports certification and accreditation, and enforces trusted relationships and government security policies. Collaborates with engineering teams and government customers on secure system development and operational deployment.