Top Remote Machine Learning Engineer Jobs

Customer-facing technical role supporting AI/ML initiatives across US Major Accounts. Architect and prototype AI/ML solutions, build MVPs and PoCs, support regional SE teams, deliver technical presentations and workshops, and upskill teams. Travel ~30-40% to support customer engagements.

Lead training, evaluation, and deployment of ML models for scene understanding and behavior prediction in autonomous vehicles.

You will train, evaluate, and deploy ML models for scene understanding and behavior prediction in autonomous vehicles, while collaborating with engineers.

The role involves developing and deploying machine learning solutions, optimizing models, and collaborating with cross-functional teams. Responsibilities include cloud architecture, algorithm engineering, and infrastructure best practices.

Design, build, and ship AI-powered observability features using LLMs and agentic workflows. Rapidly prototype, iterate with real users, integrate AI into alerting, runbooks, and developer workflows, and ensure solutions are scalable, maintainable, and production-ready while collaborating cross-functionally.

Lead and contribute to applied AI/ML projects across the SDLC: mentor teams, design and implement LLM-based solutions (RAG, copilots), architect integrations, manage data persistence choices, CI/CD, and present solutions to clients while influencing technical direction.

Lead and mentor engineering teams in applied AI/ML projects. Define architectures, propose technologies, build reference implementations, contribute code and CI/CD pipelines, and work with clients on enterprise deployments (LLMs, MLOps, RAG, vector/graph databases). Shape engineering practices and influence business development.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

The Senior ML Systems Engineer will build and maintain the training framework for large-scale language models, focusing on distributed training and performance optimization.

As a Lead Machine Learning Engineer at Root, you'll build scalable ML systems, partner with data scientists, and improve operational practices for customer lifetime value modeling.

Lead and grow a software engineering team while delivering secure, scalable software. Drive execution, mentor engineers, manage vendor integrations, implement toolchains, monitoring, automation, and support production applications. Participate in technology evaluation, cross-team consistency, and learning activities.

Design and improve ML training frameworks, modularize experiments, adapt fire-hazard detection algorithms for varied electrical environments, build scalable pipelines for high-frequency sensor data, and develop dashboards and tooling to monitor model performance and drift while collaborating across product, data science, and fire safety teams.

As a Senior ML Engineer, you'll own ML-driven features from modeling to deployment, drive innovation in generative AI, and mentor junior members.

As a Senior ML Ops Engineer, you will bridge Data Science and Engineering to automate ML workflows, manage CI/CD for ML systems, and collaborate with various teams to implement solutions for AI features and services.

Lead Android-first development of growth experiments and features, using AI-native workflows to design, implement, instrument, and analyze A/B tests. Ship performant, reliable Android experiences at scale, mentor teammates, participate in on-call, and codify AI-native engineering playbooks for broader adoption.

Design, build, and operate ML infrastructure powering data, compute, artifacts, and orchestration across cloud and on-prem. Own backend services, storage, observability, security, and developer tools; collaborate with cloud/compute providers and lead reliability and scaling efforts.

Design, develop, and deploy production forecasting models and agentic AI solutions on GCP. Perform EDA, feature engineering, experiment design, and model interpretability. Build and integrate AI agents (LangChain, Vertex AI Agents) into quotation and forecasting workflows, maintain MLOps pipelines (Vertex AI Pipelines, Cloud Composer), and present results to stakeholders.

Translate cutting-edge ML and physics research into production-grade scientific code, architect and scale large-scale simulation and training pipelines on GPU infrastructure, optimize hardware-level performance, and support models throughout the product lifecycle to accelerate in silico drug and materials discovery.

Develop and deploy machine learning models for decision-making in autonomous trucks, focusing on learned behavior systems and model performance improvements.

Develop and deploy advanced ML solutions for OCR and NLP, optimizing models for performance and integrating them into production workflows while collaborating with cross-functional teams.

The role involves designing, training, and evaluating ML models, optimizing performance, and collaborating with teams to implement AI technologies in products.

Own and improve recommendation and search systems across feed, discovery, and search. Build retrieval and ranking (candidate generation, embeddings, two-tower models), run experiments, create user/content/session representations, and ship production ML systems with monitoring to improve retention, engagement, and content distribution.