Top Machine Learning Engineer Jobs

Lead design and development of scalable, high-performance ML training infrastructure. Drive distributed training performance optimization, observability, and developer experience. Own cross-functional infrastructure initiatives, set technical direction and standards, and mentor engineers to deliver platform capabilities that support large-scale model training.

Design, implement, and optimize computer vision and deep learning algorithms for ADAS using camera feeds. Develop and validate detection, segmentation, tracking, and image-enhancement models, optimize for real-time embedded ECUs, analyze datasets, run experiments, debug defects, and integrate solutions into serial-production automotive systems.

Lead end-to-end ML and perception efforts for future sensing in autonomous vehicles: design sensor architectures, multi-modal fusion, model development, metrics, simulation-driven evaluation, system integration, and production readiness while mentoring engineers and building scalable data and training infrastructure.

Develop and evaluate ML and perception models for future sensing and autonomous driving. Work on detection, tracking, classification, sensor fusion, training pipelines, validation, robustness metrics, and integrate models into onboard systems while collaborating with cross-functional teams.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, identity abuse, merchant/marketplace risk, scams, and other adversarial activity. Own end-to-end production lifecycle: data contracts, low-latency inference, batch scoring, feature quality, deployment, monitoring, incident response, rollback, and feedback loops. Develop AI-assisted workflows and reusable decision capabilities while partnering with modelers, product, compliance, and operations.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

The AI/ML Engineer will develop and deploy AI solutions using Generative AI technologies, focusing on LLMs and frameworks like LangGraph and LangChain. Responsibilities include building APIs, deploying ML models on cloud platforms, optimizing solutions, and collaborating with stakeholders.

Lead the design and advancement of AI platform infrastructure, focusing on distributed training, efficiency, and optimization of ML models. Collaborate with teams and guide architectural decisions.

As a Staff Machine Learning Engineer, lead teams in designing AI solutions, managing projects, and interfacing with clients while ensuring technical excellence and operational effectiveness.

The Senior AI ML Engineer will design and implement AI/ML systems for healthcare, manage projects from ideation to release, and mentor engineers. Responsibilities include delivering quality code, driving architectural decisions, and collaborating with cross-functional teams.

Develop and maintain AI/ML applications, focusing on agent-based workflows, API integration, and enterprise deployment, requiring senior-level experience.

The AI/ML Engineer will develop and maintain AI/ML applications, design workflows, optimize performance, and manage data processing with a focus on LLM integration and retrieval-based capabilities.

Lead the delivery of ML initiatives, define roadmaps with cross-functional teams, and develop ML models for conversational agents and smart tools, enhancing customer support.

Build, deploy, and maintain production ML and AI systems (including LLMs, RAG, similarity search, guardrails, observability). Partner with cross-functional teams to design data pipelines, fine-tune models, monitor performance, and apply Responsible/Explainable AI best practices while contributing technical vision and roadmap for risk-focused AI products.

Design and implement agentic AI features using LLMs and generative models for conversational and web UIs. Lead end-to-end delivery, collaborate with PM/UX/GAI experts, expand product reach across customers and domains, and iterate on the Agentic AI framework to drive enterprise automation and business value.

The Senior ML Inference Engineer will design and operate a deployment platform for ML models onto autonomous vehicle hardware, collaborating with teams to enhance tools and address deployment issues.

The Senior AI/ML Engineer will design and implement Vision-Language and Vision-Language-Action models for autonomous vehicles, focusing on model optimization and collaboration with engineering teams while contributing to research and mentoring.

The Software Engineer will optimize ML infrastructure for training and inference, develop scalable systems, and work closely with ML engineers on producing high-performance models.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

As a Lead Machine Learning Engineer at Capital One, you'll design and implement ML applications, collaborate with teams, and maintain production models, leveraging cloud technologies and best practices.

The Lead MLE will design, build, and deploy ML applications, collaborating with cross-functional teams and ensuring high performance and governance of models.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

Build and maintain an ML feature platform: backend systems for feature creation, exploration, serving, data storage and availability, and offline backfilling. Deliver reliable, scalable, well-tested solutions that enable MLEs, analysts, and decisioning teams; debug large codebases and collaborate across engineering to meet availability and latency goals.

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.

Build, optimize, and scale end-to-end ML infrastructure for training, evaluating, and serving large language models. Develop distributed training and inference pipelines, model evaluation/monitoring, latency optimizations, automation abstractions, and collaborate cross-functionally to productionize state-of-the-art models.