Top Machine Learning Engineer Jobs

Build machine learning infrastructure spanning large-scale data generation, multi-GPU LLM training, training and inference optimization, and production deployment and monitoring. The role also contributes to AI detection research, publications, and technical innovation. Engineers will develop scalable data pipelines, manage distributed systems, deploy efficient LLM inference services, and collaborate across research and engineering teams in an in-person Brooklyn office.

Train and evaluate world models using real autonomous construction-machine fleet data. Explore model architectures, conduct ablations, build evaluation metrics and harnesses, and collaborate with behavior and controls teams to connect model outputs to planning and control on excavators. Present findings and establish reliable foundations for future development.

Lead the architecture and production deployment of agentic AI systems for classified and mission-critical government environments. Build systems for geospatial reasoning, retrieval, embeddings, persistent memory, orchestration, evaluation, and reliability. Own shared ML infrastructure, define technical direction, partner across engineering and research teams, mentor engineers, and drive ambiguous initiatives from prototype to supported production capability.

Develop software for autonomous vehicles that produces safe, smooth, and predictable driving behavior. Frame complex real-world scenarios as engineering problems, build algorithms and evaluation tools, scale solutions to large data pipelines, and optimize them for real-time operation in vehicles. The role involves collaborating across software, hardware, and systems engineering teams, with applications in motion planning, robotics, and machine-learning-based systems.

Develop software, algorithms, tools, and evaluation systems for autonomous vehicle maneuvering and motion planning. Solve complex real-world driving problems involving uncertainty and social interactions, scale solutions across large data pipelines, and optimize them for real-time vehicle operation. Collaborate with multidisciplinary engineering teams to ensure Waymo vehicles behave safely, smoothly, and predictably.

Designs and develops secure, production-grade AI solutions for government patent-data workflows. Responsibilities include building generative AI applications, LLM and RAG capabilities, reusable services, APIs, evaluation frameworks, and integrations. Provides architecture guidance, establishes responsible-AI controls, evaluates technical alternatives, mentors engineering teams, and supports government demonstrations, proposals, and stakeholder engagements. The role grows toward end-to-end AI architecture and technical strategy ownership.

Conduct machine learning research operations using existing infrastructure. Characterize neural network quality, failure modes, and edge cases; coordinate experiments with researchers; manage concurrent experiments and deadlines; review results; recommend theoretical or process improvements; and prepare technical reports communicating qualitative and quantitative findings.

Build and maintain scalable data platforms and pipelines that integrate structured and unstructured data. Retrieve, merge, and analyze data from APIs and databases using Python, R, and SQL. Prepare datasets for AI and generative AI models, evaluate machine learning performance, and guide multidisciplinary teams in agile client projects supporting fraud detection, cancer research, and national intelligence missions.

Develop and maintain scalable, secure data pipelines and platforms for public health data ingestion, processing, integration, analysis, surveillance, and reporting. Collaborate with data stewards and stakeholders to apply health informatics standards, governance, quality, and security practices. Support simulation and predictive modeling, scenario analysis, technology selection, and translation of analytical outputs into actionable insights. Mentor junior staff and manage cross-functional projects.

Leads enterprise AI/ML strategy, architecture, development, deployment, and productization for mission-critical initiatives. Builds and mentors engineering teams, advises customers, develops scalable machine learning solutions, and transforms prototypes into secure production systems. Oversees cloud-native architectures, MLOps, CI/CD pipelines, enterprise integration, and model lifecycle management. Supports NLP, predictive analytics, anomaly detection, computer vision, and generative AI while engaging customer and technical leadership.

Architects enterprise AI/ML systems, including training pipelines, inference services, feature stores, data ingestion, and MLOps frameworks. Leads multidisciplinary engineering teams, defines technical roadmaps, oversees execution, and interfaces with senior government stakeholders. Establishes secure engineering, DevSecOps, testing, and model governance practices while guiding cloud-native integrations, advanced ML tool adoption, responsible AI, architecture reviews, and strategic planning for Defense Intelligence Agency missions.

Develop and productionize machine learning and AI solutions for enterprise payroll products. Responsibilities include preparing large-scale HR and payroll data, building and evaluating models, prompts, and frameworks, and applying deep learning, NLP, generative AI, and agent technologies. Collaborate with product managers, software engineers, and data scientists to deliver scalable, reliable, and responsible AI capabilities supporting predictive analytics, recommendations, and payroll automation.

Develops large-scale interactive web-based visual analytics and profiling tools for AI/ML workloads. Builds visualizations for massive telemetry datasets, partners with compiler and hardware teams, and improves workflows for diagnosing machine learning performance bottlenecks. The role requires extensive experience with data visualization, developer tools, profiling systems, computing architectures, and technical user experience design.

Designs and scales production-grade data and machine learning pipelines for fraud, payment risk, and abuse prevention. Responsibilities include ETL and feature engineering, model training and deployment, data and model monitoring, incident response, code reviews, mentoring, and collaboration with data scientists, engineers, and product managers. The role supports reliable, explainable decision-making across financial and operational systems.

Design, train, deploy, monitor, and optimize speech and NLP models for a voice-agent platform. Build MLOps pipelines, APIs, and inference services while developing Android and iOS applications that integrate AI, voice, real-time communication, and backend services. Create frontend prototypes and dashboards, conduct model experiments, evaluate vendors, optimize performance, document systems, and maintain engineering quality across collaborative teams.

Build and operate production machine learning systems for Airbnb Payments, including LLM agents, real-time fraud detection, anomaly detection, personalization, and checkout optimization. Design, train, deploy, monitor, and continuously improve batch and streaming AI applications at global scale. Partner with product, engineering, operations, and data science teams, establish model governance and testing standards, and mentor engineers.

Build and improve machine learning systems for ad ranking, relevance, personalization, and optimization. Responsibilities include feature pipelines, model training and evaluation, active learning, LLM-assisted labeling and synthetic data generation, experimentation, and low-latency production inference. The role partners with product, data, and platform teams to improve advertiser performance and user engagement while maintaining reliability, latency, and data quality.

Develops AI/ML prototypes, APIs, microservices, integrations, and lightweight user interfaces for federal and DoD-aligned mission workflows. The role integrates and optimizes machine learning models for dashboards and applications, supports data ingestion and preprocessing, validates performance, and helps transition models into production. Collaboration with data scientists, architects, developers, PMO leadership, and cybersecurity teams is required. The position requires five years of relevant experience, a bachelor’s degree, ITIL v4 Foundation certification, and Public Trust or active Secret clearance.

Design, develop, deploy, and iterate production machine learning and generative AI solutions. Responsibilities include model development, NLP, prediction, anomaly detection, LLM and RAG integration, data pipelines, feature engineering, evaluation, monitoring, API integration, and architecture decisions. The role collaborates cross-functionally to balance accuracy, latency, cost, scalability, and maintainability.

Develops and deploys AI and machine learning solutions for intelligence and cyber workflows in classified environments. Responsibilities include building LLM-powered workflows and agent-based automation, integrating AI into operational systems, developing data pipelines, evaluating models, optimizing workflows, and conducting operational testing. The role collaborates with mission users, analysts, software developers, and leadership to deliver reliable, scalable, secure, and production-ready AI capabilities.

Develop and deploy enterprise-scale AI and machine learning solutions for secure government and intelligence missions. Build LLM-powered workflows, agent-based automation, data pipelines, and production ML systems. Integrate AI into operational platforms, conduct model evaluation and testing, optimize workflows, and ensure scalability, reliability, security, and governance. Collaborate with mission users, analysts, software developers, and leadership while providing technical guidance and mentoring.

Design and implement AI/ML capabilities for cyber and intelligence workflows, including LLM-powered solutions, agent-based automation, and API or enterprise data integrations. Translate mission-user workflows into automation requirements and executable logic, collaborate with analysts and developers, and ensure reliable production deployment in classified environments. The role requires Python, TS/SCI with polygraph clearance, and qualifying education and experience.

Develop and deploy AI/ML solutions for classified government and intelligence workflows. Responsibilities include building LLM-powered and agent-based automation, integrating AI into operational systems, developing data pipelines, evaluating models, optimizing workflows, and conducting operational testing. The role requires collaboration with mission users, analysts, developers, and leadership while ensuring scalable, reliable, and secure production solutions.

Designs and implements AI/ML capabilities for cyber and intelligence workflows, including LLM-powered systems, agentic automation, API and data-store integrations, and production deployments in classified environments. Works with mission users to document workflows, translate decision points into executable logic, and improve analytical efficiency. Collaborates with operators, analysts, developers, and leadership while ensuring secure, governed, reliable system integration.

Lead the design, development, and deployment of scalable machine learning and generative AI solutions. Partner with stakeholders, data scientists, and software engineers to build AI platforms, tooling, agents, and autonomous workflows. Analyze large datasets, optimize and fine-tune models, apply emerging AI technologies, and establish reusable capabilities and technical standards across the organization.