Top Remote Machine Learning Engineer Jobs

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.

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 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.

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 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.

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.

As a Staff Machine Learning Engineer, you'll enhance credit products through advanced modeling techniques, data analysis, and ML operations, supporting robust decision-making processes.

As a Staff Machine Learning Engineer, you will enhance credit modeling, manage full credit modeling stacks, and support innovations within regulated banking frameworks.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Lead development and operation of CI platform components (RBE, FUSE) to enable scalable build, test, and developer workflows. Collaborate across teams, own projects end-to-end, drive engineering best practices, mentor engineers, perform design/code reviews, and support integration of internal tools with the FUSE-based file system to improve developer productivity for AV and ML workloads.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.

Design, develop, and deploy production agentic AI systems and multi-agent orchestration for conversational healthcare. Build FastAPI and Node.js/TypeScript backends integrating LLMs (Bedrock, Vertex) via GraphQL/AppSync. Implement PII scrubbing, prompt safety, and compliance controls. Deploy containerized workloads to AWS using Terraform and CI/CD. Collaborate with product and clinical stakeholders, monitor production LLM performance and user metrics, and apply responsible AI practices.

Design, build, and deploy scalable AI/ML solutions and end-to-end ML pipelines (ingestion to deployment). Implement MLOps/LLMOps, RAG/LLM applications, agentic workflows, model monitoring, and integrate solutions into production using cloud CI/CD.

Customer-facing, hands-on AI builder shaping opportunities, prototyping AI/GenAI solutions, designing end-to-end architectures, leading discovery workshops, supporting RFPs, and enabling implementation handoffs while creating reusable AI assets and accelerators.

Customer-facing, hands-on AI builder who shapes strategic pursuits, designs end-to-end AI/GenAI architectures, prototypes demos and proofs-of-value, mentors solution leads, supports RFPs, and partners with engineering for implementation and delivery rotations.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

Design, build, and deploy production ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM features, data pipelines, observability, MLOps, and containerized deployments across cloud and on-prem environments.

Lead design and delivery of scalable Amazon Connect voice and chat solutions. Build Lambda-based integrations, integrate Lex bots and external systems, optimize routing/queues, ensure high availability and monitoring, lead production support, RCA, and continuous improvement for contact center platforms.

Lead design, development, deployment, and maintenance of AI/ML solutions (LLMs, generative models, predictive analytics) and conversational agents. Integrate AI features with web/APIs and cloud infrastructure, ensure responsible AI, security, and operational excellence, mentor team members, and partner with product and architecture to deliver scalable healthcare solutions.

Lead design, build, and operate end-to-end AI/ML-enabled full-stack applications for clinical workflows. Develop Next.js/React frontends, Node.js backends, PostgreSQL data models, integrate LLM capabilities, deploy and monitor on Azure, implement CI/CD and security best practices, and mentor engineers.