Top Remote Machine Learning Jobs

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.

Develop and evaluate novel AI/ML solutions for healthcare using structured and unstructured data. Build and test deep learning and classical ML models, design experiments, perform statistical analysis and visualizations, document models, collaborate with stakeholders, RUAI, and engineering to productionize solutions and MLOps pipelines, and publish/patent research.

The Staff Machine Learning Engineer will lead AI initiatives, architect LLM systems, design evaluation frameworks, and mentor engineers.

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

Lead end-to-end research applying advanced ML/DL (transformers, GNNs, RL, ensembles) to generate alpha across digital asset markets, build production-grade pipelines, prevent leakage/overfitting, analyze microstructure and on-chain data, and mentor junior researchers.

As a Senior Machine Learning Engineer, you will develop machine learning models, automate data pipelines, and collaborate with teams to meet customer needs.

The Senior Machine Learning Engineer will develop and implement machine learning models, work with stakeholders, and drive AI solutions for complex data engineering challenges while ensuring high-performance operations.

As Director of AI/ML, lead the ML organization, set technical vision, mentor a high-performing team, and contribute to model development and production deployment.

The AI/ML Engineer will design and implement machine learning algorithms, analyze data, collaborate with teams, optimize models, and present findings.

The Applied Scientist will transform raw data into actionable insights by developing machine learning models for personalization, ranking, and optimization to enhance conversion and revenue. This role emphasizes building production systems, frameworks for experimentation, and communication with team members regarding technical challenges.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

Lead the machine learning strategy, design scalable ML systems, drive MLOps, mentor engineers, and comply with AI governance.

The role involves developing quantization and compression strategies for AV models, conducting numerical sensitivity analyses, and creating automated tooling for low-precision deployments, emphasizing safety and performance.

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, scams, and other abuse. Integrate diverse signals into low-latency serving and batch scoring, own feature pipelines and model lifecycle, develop AI-assisted triage and feedback loops, and partner cross-functionally to balance fraud reduction with legitimate customer access.

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 Senior ML/AI Engineer will enhance developer productivity for AV engineers at GM by designing, implementing scalable, reliable systems and mentoring team members.

The AI/ML Engineer will design, develop, and deploy machine learning algorithms and large language models while collaborating with stakeholders to meet business needs.

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.

This role involves designing, deploying, and maintaining machine learning and AI models in production, managing ML pipelines, and ensuring system reliability and performance.

As a Senior Machine Learning Engineer, you will lead model validation for AI systems, challenge model soundness, and build validation tools for high-stakes areas such as credit and fraud prevention.

As a Senior Machine Learning Engineer, you'll design scalable AI/ML infrastructure, optimize model training performance, and collaborate with teams to implement advanced AI solutions.