Top Machine Learning Engineer Jobs

Build, train, deploy, and maintain large-scale machine learning models and AI agents for marketing and customer experiences. Develop conversational AI, generative AI, recommendation, retrieval, ranking, and transformer-based systems. Own ML product lifecycles, including success metrics, A/B testing, production operations, and iterative improvement. Collaborate with Engineering, Product, Design, customers, and stakeholders while leading technical projects and mentoring teammates.

Develop and deploy models to understand technical drawings and specifications, perform multimodal reasoning across diagrams, text, and structured data, translate research into production within ~1 year, present prior research, and collaborate closely with founders to deliver customer-impacting applied research.

Build production-grade AI and agentic systems for healthcare workflows. Responsibilities include designing data pipelines, evaluation harnesses, model deployment and monitoring systems, and scalable LLM- and vision-based applications. The role spans backend and full-stack development, AI agent orchestration, experimentation, training pipelines, and frontend integration while collaborating with ML researchers and engineering teams.

Designs, trains, evaluates, and deploys ML and AI models; implements MLOps for production, monitors model performance, collaborates with data scientists and stakeholders, and optimizes scalability and reliability.

Develop AI/ML-powered control systems, digital twins, simulation environments, and machine learning models for data center cooling equipment. Build reduced-order models, maintain FMU packages, investigate reinforcement learning and model-predictive control strategies, and deploy inference pipelines on Linux platforms. Integrate equipment and simulations using industrial protocols including MQTT, Modbus, and CAN while collaborating with thermal, mechanical, and controls engineers.

Develop and productionize machine learning models for industrial process optimization using spectral and multimodal sensor data. Responsibilities include designing experiments, building preprocessing and feature extraction pipelines, monitoring model and sensor drift, detecting anomalies, developing ML infrastructure, and applying chemometrics, hybrid modeling, self-supervised learning, probabilistic reasoning, and time-series techniques.

Build and ship AI/ML products for finance and accounting workflows. Responsibilities include transforming messy financial data into production systems, designing evaluations, analyzing errors, optimizing quality and cost, and partnering with product and engineering. The role requires practical experience with machine learning, LLM applications, retrieval, embeddings, agentic systems, noisy data, and end-to-end production deployment.

Build production ML infrastructure for humanoid robotics, including multimodal data curation, annotation, versioning, large-scale pipelines, simulation and evaluation harnesses, model registries, deployment promotion, experiment tracking, and developer tooling. Partner with autonomy, data platform, and teleoperation teams on lifecycle contracts and technical direction while mentoring engineers through code and design reviews.

Lead the architecture and implementation of a Kubernetes-native AI Factory platform for distributed training, simulation, evaluation, inference, and deployment. Build GPU infrastructure, scheduling, storage, networking, observability, model lifecycle capabilities, and self-service developer workflows across cloud, on-premises, sovereign, and air-gapped environments. Partner with ML researchers and autonomy teams while providing technical leadership on scalable, maintainable AI infrastructure.

Design and implement machine learning research systems for generative models, causal inference, planning, and multi-objective optimization. Build training and inference pipelines, dataset loaders, experiment tracking, versioned datasets, and containerized model components. Develop synthetic data campaigns, translate research prototypes into tested production code, present findings, document results, and collaborate with RF, DSP, formal methods, academic, and government stakeholders.

Provides senior systems engineering leadership for integrating AI/ML capabilities into MQ-9 unmanned aviation systems. Develops requirements, architectures, implementation strategies, roadmaps, trade studies, technical evaluations, and engineering analyses. Oversees AI/ML data management, model validation, integration, testing, and deployment while coordinating government, industry, and engineering stakeholders. Supports defense acquisition, fielding, sustainment, deficiency investigations, and executive-level technical decision-making within the NAVAIR program environment.

Build and operate scalable CI/CD, GitOps, and monorepo infrastructure for frontier models and custom AI accelerators. Design regression, benchmarking, release qualification, orchestration, artifact management, observability, and capacity-control systems. Partner with model, compiler, kernel, runtime, firmware, validation, and hardware teams to automate production-readiness gates, improve reliability and developer productivity, and shorten feedback loops.

Design, build, and productionize scalable machine learning systems for personalized health and fitness metrics using physiological and behavioral data. Responsibilities include improving ML pipelines, translating research prototypes into reliable production services, developing operational infrastructure, implementing observability and incident response, and mentoring engineers and applied scientists.

Lead exploration and application of large language models and generative AI for enterprise contact-center automation. Translate research into high-performing, deployable ML systems; contribute across experimentation, implementation, and deployment; collaborate with engineering and product teams; optimize performance; and help shape team strategy, innovation, and professional growth.

Lead exploration and application of large language models and generative AI, translate research into deployable ML systems, and guide strategic direction. Develop solutions across experimentation, implementation, and deployment while collaborating with engineering and product teams to optimize performance and customer outcomes in contact center automation.

Leads the end-to-end execution layer for machine learning, including data pipelines, large-model training and fine-tuning, evaluation, inference, deployment, monitoring, and reliability. Designs scalable GPU-based systems, optimizes performance and cost, partners with research and application engineering, and translates model capabilities into measurable production improvements. Also provides technical leadership, judgment, and support for high-impact ML delivery.

Own and operate models in a production AI research platform: select and fine-tune models, build training/evaluation/extraction pipelines, implement eval harnesses and CI, redesign model routing for accuracy/cost/latency, and drive end-to-end product-quality and verification for finance workflows.

Set the technical direction for machine learning across underwriting, fraud, risk, and personalization. Own the ML platform and modeling roadmap, including feature stores, pipelines, serving, monitoring, governance, and experimentation. Lead high-impact credit and cash-advance models through deployment and iteration, establish model-risk standards, partner with business and compliance leaders, and mentor engineers. This is a hands-on staff role with company-wide technical influence.

Develop deep learning models using imitation and reinforcement learning to generate autonomous vehicle trajectories. Build production ML pipelines, metrics, and error-analysis tools; evaluate driving plans for safety, progress, and comfort; and collaborate with Planning, Perception, Simulation, and Systems Engineering teams to improve autonomous driving in complex urban environments.

Develops and deploys predictive analytics, machine learning, and deep learning solutions for production use. Responsibilities include statistical analysis, feature engineering, model development, anomaly detection, NLP, computer vision, MLOps, CI/CD pipelines, cloud AI platform integration, data engineering collaboration, model monitoring, governance, and stakeholder communication. The role also supports large-scale analytics, AI modernization, and human-reviewed multi-source summarization.

Own machine learning at Metriport end to end, including problem framing, feature engineering, model training, evaluation, deployment, monitoring, and infrastructure. Build predictive models and document-processing systems from large, messy clinical datasets. Develop training and inference pipelines, experiment tracking, model versioning, and production monitoring. Collaborate with founders, engineers, and customers to identify high-value ML opportunities and deliver reliable healthcare intelligence.

Develop scalable AI software by translating Python research code into performant Rust components. Build extensive automated tests, runtime services, simulations, interfaces, and deployment tooling. Profile and optimize CPU, memory, latency, and throughput while supporting concurrency, interoperability, containerization, observability, cloud, on-premises, and edge environments. Collaborate with researchers and engineers on algorithms, documentation, production validation, and confidential intellectual property.

Design, develop, deploy, and improve real-time machine learning systems for Lyft’s Fulfillment team. Build feature pipelines, training workflows, and model-serving infrastructure; translate research into production; evaluate models against business KPIs; run experiments; write production code; conduct code reviews; and collaborate with product, data science, and engineering teams.

Leads development of BEV and multi-modal perception models for autonomous trucks, combining camera, LiDAR, radar, and HD map data. Responsibilities include model architecture, large-scale distributed training, data pipelines, robustness evaluation, sensor fusion, research into foundation models, and mentoring ML engineers. The role requires deep expertise in 3D vision, autonomous systems, multi-view fusion, and production-scale machine learning.

Build and deploy computer vision and machine learning solutions from research prototypes into scalable production services. Develop MLOps pipelines for data preparation, training, validation, versioning, deployment, and monitoring. Train and optimize deep learning models, integrate them into secure cloud systems, and support national security missions. Collaborate with mission partners, engineers, and domain experts while applying containerization, DevSecOps, model governance, and inference optimization practices.