Top Remote Machine Learning Jobs

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

As an Open-Source Machine Learning Engineer at Hugging Face, you'll improve ML libraries, work with the community, and contribute to open-source projects.

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.

The Machine Learning Engineer will execute the full ML lifecycle, collaborating on model implementation and improving customer experiences through data-driven insights.

Develop, deploy, and integrate machine learning and deep learning models for scientific and industrial problems (materials, chemistry, process optimization). Build data pipelines, feature engineering, model evaluation, and scalable production integrations. Collaborate with scientists, engineers, product, and customer teams to translate domain problems into AI solutions, support deployments, and communicate results. Conduct strategic research and stay current with AI/ML methods; periodic customer travel.

Build ML prototypes and data pipelines, collaborate with researchers and product engineering to integrate models into Fusion, containerize and deploy applications, implement best practices, and document architectures to accelerate research-to-production workflows.

The Senior Machine Learning Engineer will build and optimize ML systems for CRM marketing, collaborating with cross-functional teams to impact customer engagement and product functionality.

Design, develop, and deploy ML solutions for localization workflows (machine translation, LLM fine-tuning, QA). Own projects end-to-end, implement and optimize models using Python and ML frameworks, deploy via Docker and AWS (SageMaker/EC2/S3), monitor production, document experiments, and collaborate across teams.

Lead a Machine Learning engineering team owning mission, execution, morale, and code quality. Partner with product to deliver roadmap, remove roadblocks, improve processes, coach and grow engineers, and drive development, deployment, and innovation of ML models for recommender, search, and matching systems.

Senior ML Engineer joins a product team to design, build, deploy, and monitor production-grade AI/ML solutions. Responsibilities include algorithm and software design, pairing with teammates, creating scalable data pipelines and infrastructure, implementing model serving and monitoring, performing performance tuning and testing, and supporting product lifecycle and stakeholder collaboration.

Develop and validate machine learning detections to detect, prevent, and respond to cheating in Call of Duty. Build and improve production ML models, run experiments and analyses, manage large datasets and ETL processes, and collaborate with game designers, engineers, and stakeholders to measure impact and operationalize anti-cheat solutions.

Design, build, and deploy production ML and GenAI solutions from prototyping through serving. Apply supervised/unsupervised/deep learning and NLP to structured and unstructured data, implement data pipelines and feature engineering, evaluate model performance/bias/drift, and collaborate with engineers and product teams to integrate models into services and APIs while contributing to architecture, scalability, and documentation.

Lead development of ML components for medical image reconstruction: define training/evaluation pipelines and metrics, productionize models (inference performance, monitoring, safe fallbacks), collaborate on hybrid physics-ML algorithms, and build tooling for experimentation and rigorous verification.

Own end-to-end ML model lifecycle from EDA and feature engineering to production deployment and monitoring. Collaborate with cross-functional teams to identify modeling opportunities, evaluate performance and business impact, integrate new data sources, and ensure scalable, observable production systems.

As a Fraud ML Engineer, you will ensure authentic human data is processed by building detection systems using adversarial ML techniques and managing backend systems.

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 an AI/ML Engineer, you will design and deploy models for real-world applications, optimize data pipelines, and engage with global leaders to enhance AI technology's impact across sectors like healthcare and government.

As a Machine Learning Engineer, you will design, build, and deploy ML models for underwriting at Stripe Capital, ensuring performance while collaborating with multiple teams.

As a Machine Learning Engineer at Yobi, you will design and optimize systems for real-time model serving and inference, ensuring performance and reliability.

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

Build and re-architect Shipt's LLM-native search stack including retrieval and ranking systems. Design multi-task and multi-objective models, integrate personalization signals, develop hybrid dense/sparse retrieval, and apply generative retrieval and query expansion. Analyze search data, report findings to leadership, and partner with senior scientists and ML engineers to improve relevance, revenue trade-offs, and operational metrics.

Lead a team of data scientists developing ML-driven products, managing infrastructure, training models, and ensuring production quality while collaborating with senior leadership.

The Senior Machine Learning Engineer at Grailed will enhance personalization and recommendations, develop AI/ML solutions, and collaborate across departments for data-driven insights.

Design, build and deploy production AI/ML solutions from complex, distributed farm data. Engineer features, select models, run experiments, move models to scalable real-time production, monitor and observe AI systems, evaluate emerging AI technologies, and collaborate with product, engineering and domain teams. Contribute to user-facing features with React/TypeScript as needed.

Design, build, and deploy production-grade AI/ML solutions that convert complex, distributed farm data into actionable intelligence. Engineer features, select models, run experiments, and move models to scalable, real-time production. Integrate, fine-tune, monitor, and observe models on cloud infrastructure (AWS, Databricks). Partner with product, engineering and domain teams and contribute to user-facing experiences when needed.