Top Machine Learning Engineer Jobs

Design, build, and optimize scalable ML infrastructure for training, evaluating, and serving large language models. Build abstractions, automation, microservices, and ETL pipelines; collaborate cross-functionally to productionize, monitor, and optimize LLMs and related ML systems.

Design, build, deploy, and maintain production ML models and data pipelines at scale. Collaborate with product and data science teams, optimize ML infrastructure, apply CI/CD, monitor model performance, and ensure responsible, explainable AI practices.

Research, design, develop, and test machine learning and robotic applications for autonomous vehicles across the full ML lifecycle. Create novel deep learning models (CNNs, generative AI), improve perception (kinematics, intent, trajectory), integrate multi-sensor data (camera, RADAR, LiDAR) with HD maps, develop auto-labeling and large-scale data pipelines, optimize models, and debug real-time perception issues in a ride-hail fleet.

Lead the design, development, and deployment of AI software components while partnering with engineers and scientists to deliver innovative AI solutions at Capital One.

Lead design, development, and operationalization of enterprise-scale AI/ML solutions across hybrid environments. Architect scalable, secure AI infrastructure (GCP Vertex AI, Azure ML, On-Prem), manage full model lifecycle, drive migration and modernization, implement monitoring/drift detection, integrate generative and agentic AI, automate governance, mentor teams, and advise senior leadership on AI strategy.

Lead design, development, and operationalization of enterprise-scale AI/ML systems across hybrid environments. Drive platform architecture, model lifecycle management, migration to cloud-native platforms, generative and agentic AI integrations, automation and governance, cross-functional collaboration, and mentorship to accelerate predictive and generative AI adoption.

Lead design and delivery of scalable ML and data pipelines on GCP. Build reusable frameworks, feature stores, orchestration and CI/CD, ensure data quality, monitoring, cost optimization, and enable self-service for data scientists across the ML lifecycle.

Lead design and delivery of scalable, secure GCP data and ML pipelines. Build feature stores, standardized transformation libraries (Python/SQL/Beam), orchestration (Composer/Airflow), CI/CD, model training/validation/monitoring, enforce data quality/lineage/observability, optimize cost/performance, and enable self-service for data scientists with documentation and runbooks.

Lead architecture, development, and operationalization of enterprise-scale AI/ML across hybrid environments. Drive platform strategy (GCP Vertex AI, Azure ML, On‑Prem), model lifecycle management, migration to cloud-native platforms, generative/agentic AI integration, automation of governance, and mentor engineering teams while advising senior leadership on AI strategy.

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.

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.

The Principal Engineer, AI/ML Architecture will define the technical direction for Coupa's AI platform, focusing on model training, evaluation, safety standards, and mentoring a team of ML and data engineers.

The Senior Machine Learning Engineer designs and develops ML systems, collaborates with teams to meet analytical needs, and optimizes large-scale ML solutions while setting monitoring and best practices.

The Senior Machine Learning Engineer will design and implement ML systems, collaborate with data scientists, and enhance analytics capabilities to improve game design and player engagement.

Design, implement, and deploy scalable ML training and evaluation platforms. Own end-to-end projects, make architectural decisions, drive prioritization, collaborate cross-functionally, and mentor engineers to accelerate autonomous driving model development.

Build and deploy scalable ML training and evaluation platforms, drive technical projects and architectural decisions, collaborate with partner teams, prioritize platform improvements, ensure reliability and performance, and mentor junior engineers.

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

Lead development of scalable ML infrastructure, focusing on compute for simulation, data labeling, and data generation. Collaborate with ML engineers, set technical roadmaps, ensure system reliability, and research advanced computing techniques.

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

The Senior ML Infrastructure Engineer will design, implement, and optimize computing platforms for simulation workflows, ensuring high reliability and performance. Responsibilities include leading technical decisions, collaborating with ML engineers, and enhancing system efficiency.

The Lead Machine Learning Engineer will design, build, and deploy machine learning models and data pipelines, ensuring high performance and governance of ML applications while collaborating in an Agile environment.

The Lead Machine Learning Engineer designs and implements ML applications, builds data pipelines, collaborates with teams, and ensures performance and governance of ML systems.

Design, build, and optimize large-scale ML infrastructure: embedding generation, batch inference, data storage/compute, data management, quality systems, and production deployments with ML engineers to improve ranking and recommendation systems.

Design, deploy, and maintain end-to-end ML-driven risk solutions at scale to detect and prevent fraud, abuse, and credit risk. Lead technical decisions, build ML tooling and processes, apply state-of-the-art models and third-party data, investigate emerging risk patterns, and collaborate with platform and cross-functional teams to ensure reliable real-time model operation.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.