Top Machine Learning Engineer Jobs

Develop ML and multimodal AI systems that integrate neurophysiological, behavioral, and sensor data for cognitive state estimation and decision support. Build algorithms and prototypes, apply cognitive science to improve explainability and human-AI collaboration, contribute to proposals/publications, and demonstrate capabilities for mission-driven R&D.

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.

Lead architecture and technical strategy for enterprise-scale agentic AI systems and AI Control Tower. Define governance, orchestration, observability, evaluation, security, and developer experience. Prototype multi-agent patterns, guide cross-domain decisions, mentor senior engineers, and convert Customer Zero feedback into product improvements.

Lead end-to-end design, implementation, deployment, and operation of production AI/ML-enabled full-stack applications. Build LLM-powered features, design scalable backend services and data models, deploy on Azure, implement CI/CD and observability, and mentor engineers while ensuring secure, compliant integration into clinical workflows.

As an AI Engineer, you will develop and optimize AI systems for legal tasks, work on integrating models, and establish evaluation frameworks to enhance decision-making capabilities in complex legal workflows.

Design and develop AI solutions, manage data flow for AI deployment, optimize models, and conduct data analysis on large datasets for operational improvement.

The AI/ML Engineer designs and develops AI solutions, optimizes models, manages data flow for deployment, and conducts data analysis to enhance operational outcomes for mission operations.

The Principal AI/ML Engineer will lead the AV ML Infra team, overseeing scalable ML infrastructure, project ownership, collaboration, and team mentorship in AI and ML innovation at GM.

As a Staff AI/ML Engineer, you'll enhance developer productivity, mentor engineers, drive best practices, and implement scalable solutions for AV tools and services.

Design, build, and maintain generative AI and AIOps solutions for banking. Lead development and deployment of LLM-based systems, create POCs, write secure production code (Python/Java), build UIs with React/Angular, apply AWS and CI/CD best practices, and ensure operational stability and automation.

Lead design, development, and deployment of generative AI/LLM solutions and AIOps. Architect and build production-grade AI models, create UI components (React/Angular), apply AWS best practices, ensure CI/CD, resiliency, and security, and drive automation and operational stability.

Lead design, development, and deployment of generative AI solutions and AIOps; build and productionize AI models; create UIs with React/Angular; apply AWS best practices; automate operations; participate in communities of practice and deliver secure, high-quality production code.

Lead technical direction and people management for an AI/ML engineering squad. Design, build, and deploy production-grade ML and Generative AI solutions (LLMs, RAG, agentic systems), drive MLOps/CI-CD, ensure Responsible AI and HIPAA-compliant practices, conduct architecture/code reviews, and mentor engineers to deliver measurable business outcomes.

Build and maintain production ML systems: training and inference pipelines, model packaging and serving, feature pipelines, monitoring for drift and degradation, automated retraining, and reproducibility/versioning across data and models.

Design and own the verification engine that automates product truth verdicts: build confidence-scoring pipelines, adversarially robust models, a human-graded golden exam, and arbitration rulebooks. Raise machine-settled verdicts from 40% to 80% while maintaining 95%+ accuracy, embed eval-driven development, and scale solutions to millions of claims in production.

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.

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.

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.

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.

Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.

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.

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.

Lead design, development, and enterprise deployment of scalable production ML/AI solutions for pharmacy services. Own MLOps pipelines, model training, validation, containerized deployment, monitoring, and architecture. Collaborate with data scientists, engineers, and business leaders, evaluate emerging AI/LLM technologies, and provide technical leadership and mentorship.

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.