Top Machine Learning Engineer Jobs

Design, build, and optimize scalable ML infrastructure for training, evaluation, and high-performance inference. Develop feature generation/serving pipelines, data management and labeling systems, and collaborate with ML engineers to deploy production models while ensuring reliability, scalability, and production-quality code.

Lead ML engineer responsible for productionizing ML systems: design, build, deploy, monitor, and scale models and data pipelines on cloud (AWS). Collaborate cross-functionally, apply MLOps best practices (CI/CD, testing, governance), and maintain high availability and explainable AI standards.

Design, build, deploy, and maintain production ML systems at scale. Collaborate with product and data science teams to create ML architectures, data pipelines, model training and monitoring, CI/CD for models, and ensure performance, reliability, governance, and Responsible/Explainable AI practices.

Develop, deploy, and operate production-grade Nextflow pipelines and cloud-based omics data platforms. Integrate and harmonize complex biological and clinical datasets (single-cell, bulk transcriptomics, ATAC-seq, proteomics, genetics) for ML model training and inference. Implement DevOps and CI/CD practices, manage pipeline lifecycle, collaborate with scientists and external partners, and deliver reusable, scalable data products to support AI-driven discovery.

Lead design and implementation of LLM observability features: prototype and scale product capabilities for tracing, evaluating, and debugging generative AI systems. Work cross-functionally to influence architecture, mentor engineers, prioritize customer pain points, and drive product and engineering decisions for reliable, high-performance AI observability.

Lead design, build, and productionize large-scale ML systems (including RL-based recommenders). Collaborate with Product and Data Science to create data pipelines, cloud architectures, CI/CD, model retraining, monitoring, and Responsible/Explainable AI practices. Mentor and lead teams to deliver performant, resilient ML applications across web, mobile, and email.

Drive technical strategy for the Personalization Platform, develop recommendation systems, maintain ML workflows, and innovate AI solutions for customer engagement.

Lead design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

Senior individual contributor designing and deploying onboard ML solutions for autonomous driving. Develop models (detection, tracking, trajectory, safe AI), build training pipelines and deployment tooling, translate research into production, collaborate cross-functionally, and mentor engineers.

Design, implement, and deploy ML models for autonomous driving (detection, tracking, trajectory generation). Apply advanced techniques (imitation learning, RL, foundation models), collaborate cross-functionally, and support production ML systems and model deployment.

Build and scale Gusto's ML/AI platform: design MLOps solutions, automated pipelines, model development frameworks, deployment, monitoring, CI/CD, and observability; collaborate with ML engineers and apply AI tools and best practices to production ML systems.

Lead design, build, and operate scalable ML training platform on cloud. Optimize GPU single-node and distributed training, manage Kubernetes-based infrastructure, enable GenAI/LLM fine-tuning workflows, implement observability, improve developer CI/CD and automation, and enforce governance, security, and responsible AI practices.

Lead design and productionalize ML algorithms and pipelines to detect account-sharing fraud in real time. Build scalable preprocessing, training, deployment, monitoring, and A/B testing solutions with cross-functional teams to optimize detection while minimizing user disruption.

Lead design, build, deploy, and maintain production-grade ML/data platforms and end-to-end model pipelines. Engineer large-scale data ingestion, transformation, storage (data lakes/warehouses), monitoring, CI/CD, and production reliability. Resolve production incidents quickly, add features, and drive adoption of enterprise AI-assisted engineering practices while ensuring responsible AI, security, and validation standards across the team.

Lead design, deployment, and operation of production ML systems and inference pipelines. Build and scale APIs and services on Kubernetes, optimize pipelines, monitor model performance and drift, and partner with cross-functional teams to deliver ML insights to customers.

Design and implement AI/ML systems for healthcare, own inference and delivery, manage/scale production ML systems, build scalable data pipelines and APIs, apply MLOps practices (deployment, monitoring, drift detection), and work with large-scale data and distributed computing frameworks.

Design, train, and implement domain-specific LLMs for automation in restoration companies. Collaborate with teams, coordinate AI engineering, and ensure model reliability.

Design and build end-to-end software products for autonomous vehicles, leading projects in full-stack development, cloud infrastructure, and machine learning pipelines while mentoring junior engineers.

Lead ML Engineer for Finance Tech to design, build, deploy, and maintain AI/ML systems (including LLMs and RAG). Partner cross-functionally to create production data pipelines, model evaluation, governance, observability, and responsible AI practices while contributing technical vision and scaling AI infrastructure.

As a Staff AI/ML Full-Stack Engineer at GM, you'll design and develop end-to-end software products, own technical projects, and mentor junior engineers while focusing on cloud infrastructure and scalable applications.

Develop and implement AI and ML systems for Defense and Intelligence sectors, focusing on deployment, scalability, and integration of technologies.

Design, develop, and deploy production-grade AI/ML systems (NLP/LLMs, CV, generative models). Build scalable data pipelines, microservices/APIs, and cloud/edge deployments; optimize, version, and evaluate models; integrate solutions into classified operational environments and produce technical documentation.

Design, create, and implement AI and ML systems for Defense and Intelligence sectors, ensuring scalability and integration into various environments.

Lead technical direction and deliver large-scale ML platform infrastructure including feature store, model serving, experimentation, and training pipelines. Drive architecture, mentor engineers, coordinate cross-team initiatives, resolve system-wide issues (latency, reliability, GPU utilization), and define safe ML deployment paths from registration through rollout, monitoring, and rollback.

Lead design, development, and production deployment of scalable ML systems across domains. Own end-to-end ML lifecycle, architect pipelines and serving, drive MLOps maturity, mentor engineers, conduct design/code reviews, and ensure monitoring, governance, and model reliability in production.