Top Machine Learning Engineer Jobs

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Design, build, and operationalize reproducible AI/ML pipelines and automation for mission-critical systems. Troubleshoot CI/CD and containerized workloads, integrate AI capabilities with infrastructure and security, and support resilient hybrid compute operations to enhance warfighting decision support.

Build and deploy production generative AI and machine learning systems for utility clients. Own engagements end to end, including discovery, architecture, retrieval, model selection, evaluation, deployment, monitoring, operator-facing applications, and post-go-live support. Integrate with client data and business systems, establish AI safety and governance controls, manage scope and risk, and communicate with technical teams and executives. Contribute reusable accelerators, evaluation frameworks, and reference architectures across client engagements.

Build and operate production machine learning platforms and pipelines, including deployment, monitoring, retraining, governance, and lifecycle management. Maintain and improve enterprise sales forecasting models by diagnosing accuracy degradation, engineering features, tuning algorithms, and evaluating performance. Deploy workloads on Vertex AI and BigQuery, operationalize generative AI, manage infrastructure as code, ensure data and model lineage, troubleshoot failures, and collaborate with Data Science, Engineering, Security, Finance, Operations, and Supply Chain.

Leads the design, development, validation, deployment, monitoring, and tuning of machine learning pipelines and models for IoT product performance, installation quality, outage detection, and fleet health. Builds predictive, classification, time-series, and anomaly-detection solutions using AWS, Elasticsearch, Redshift, and large-scale sensor data. Defines project scope and validation standards, collaborates cross-functionally, reports findings, supports production integrations, and mentors engineers.

Develop and maintain the U.S. Census Bureau’s LLM Autocoder using Python, LLMs, prompt engineering, text classification, structured outputs, and testing. Build survey-specific pilots, troubleshoot production issues, evaluate model changes, and collaborate with data scientists and MLOps engineers on AWS deployment, rollback, and verification. Maintain documentation and support government handoff while meeting Census security and operational requirements.

Design, build, evaluate, and deploy AI agents and machine learning automation for engineering, product, delivery, and customer-facing workflows. Integrate AI into existing software, collaborate with technical and healthcare stakeholders, optimize systems for accuracy, latency, cost, and safety, and document development processes. The role emphasizes LLMs, agentic frameworks, production ML systems, human-in-the-loop safeguards, and healthcare-aware solutions.

Design, build, and operate production ML models, services, and platforms for pricing, risk, and decision systems. Own end-to-end model pipelines, deployment, monitoring, and iteration. Collaborate with researchers and product to productionize prototypes, tackle data drift and sparsity, lead design reviews, and mentor teammates.

As a Senior AI/ML Engineer, you will manage the AI/ML lifecycle, develop predictive models, conduct anomaly detection, and integrate AI capabilities into the Confido platform.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Own the reliability, performance, observability, and cost-efficiency of production machine learning systems. Architect Garner’s ML platform, including feature stores, model registries, model-serving infrastructure, and CI/CD pipelines. Establish automated data-quality, model-validation, drift-monitoring, and incident-response processes. Set technical direction, develop platform standards and KPIs, and prepare for future continuous-training capabilities while collaborating with ML, data, platform, and product engineering teams.

Lead the technical direction and hands-on development of ML and operations research systems powering delivery dispatch. Build pricing, quote selection, forecasting, routing, batching, fleet recommendation, LLM, optimization, evaluation, and MLOps capabilities. Translate operational constraints into reliable models and solver formulations, establish production ML standards, and mentor engineers. The role partners with Product and leadership to deliver measurable improvements in revenue, margin, utilization, conversion, and delivery reliability.

Build systems that capture professional design activity and convert it into training-grade preference data. Own product instrumentation, pipelines from canvas to warehouse to training sets, dataset contracts, lineage, reproducibility, privacy boundaries, consent, and feedback collection. Partner closely with product engineers and researchers to ship new signals, make ambiguous behavioral data accurate, and ensure datasets and training results are traceable and repeatable.

Build and optimize end-to-end search and discovery systems, including machine learning pipelines, serving infrastructure, and relevance solutions. Develop AI products using deep learning and large language models, improve search quality, deliver complex systems with measurable business impact, collaborate across teams, remove technical blockers, and recruit and develop engineering talent.

Lead Security Engineer designs, develops, and validates secure software solutions, performs threat modeling, vulnerability and penetration testing, and implements AI-assisted security workflows. They develop secure production code, review peers' code, work with stakeholders to mitigate risks, and apply MLOps/GenAI controls to ensure resiliency, auditability, and regulatory compliance across cloud environments.

Build and operate production-ready AI/ML systems by integrating models into applications, optimizing latency, throughput, reliability, and resource efficiency, and supporting deployment, monitoring, and troubleshooting. The role requires MLOps, cloud and DevOps familiarity, production deployment experience, and strong software engineering, machine learning, system architecture, and communication skills. Engineers will collaborate cross-functionally, lead technical projects, and help ensure enterprise security, compliance, observability, and operational readiness.

Develops full-stack AI and machine learning software using Python, Java, Vue.js, Node.js, cloud platforms, databases, containers, and CI/CD practices. The role requires software engineering experience on complex programs, testing and debugging capabilities, Agile development experience, DoD8570 IAT2 compliance, and an active TS/SCI clearance with polygraph.

Develop and operate production AI/ML systems, including distributed model infrastructure, automated evaluation and testing, GPU optimization, secure CI/CD, and performance analytics. The role owns features end to end from architecture through production, performs model benchmarking and error analysis, and manages reliable deployment across multi-node clusters.

Develop and apply machine learning, controls, uncertainty quantification, sensor fusion, and autonomy algorithms for chemical and electric space propulsion systems. The role includes data analysis, algorithm development, SIL/HIL testing, remote control of test articles, autonomy hardware integration, and experiment reporting. The engineer will support basic research, applied technology development, and flight demonstrations while collaborating with multidisciplinary teams.

Lead the design and development of perception foundation models for autonomous vehicles. Build deep learning systems for 3D scene understanding, sensor fusion, multimodal data analysis, training, evaluation, deployment, and inference optimization. Collaborate across perception, motion planning, simulation, infrastructure, and tooling teams while applying rigorous safety standards and agile practices.

Design, build, and operate large-scale online ML inference infrastructure to serve production models with low latency and high reliability. Optimize inference performance, enable distributed training workflows, integrate ML pipelines with orchestration systems, improve observability, and support safe model rollout, canary testing, and automated rollback. Lead architectural improvements and collaborate with ML engineers and product teams to scale, monitor, and streamline model deployment and iteration.

The Founding ML Research Engineer will design and evaluate ML models, develop experimentation pipelines, and contribute to AI research while optimizing training processes.

The Founding ML Engineer will build and scale machine learning systems, focusing on ML pipelines, model implementation, and collaboration with teams to drive measurable impact.

Lead design and delivery of agentic and generative AI systems for banking: translate business problems into research and engineering plans, build multi-agent solutions, design reusable services and evaluation practices, mentor researchers/engineers, and partner to scale secure, reliable production deployments.

Build and optimize batch and streaming data pipelines for AI, analytics, and enterprise platforms. Develop scalable transformations across cloud and lakehouse environments, implement data modeling, lineage, metadata, quality controls, and orchestration, and resolve complex performance and reliability issues. Partner with cross-functional teams to deliver secure, governed data solutions while mentoring engineers and leading modern data engineering initiatives.