Top Remote Machine Learning Jobs

Lead development and refinement of machine learning pricing models (especially reinforcement learning) for Airbnb hosts. Collaborate with data scientists, product, and engineers to design, test, integrate, and monitor models, analyze performance, and prioritize projects to improve host outcomes.

Design, build, and deploy production ML models (time series forecasting and NLP). Perform EDA, feature engineering, model evaluation, and translate business problems into ML solutions. Maintain production model-serving pipelines and collaborate across teams to deliver scalable data-driven systems.

Build and prototype supervised ML models to predict ad campaign delivery outcomes (reach, frequency, contention). Own feature engineering, evaluation frameworks, interpretability, and partner with ML engineers to deploy and monitor models at scale. Collaborate across product, engineering, and sales to drive adoption and measure lift versus simulation baselines.

Lead the design and delivery of a production-grade localization ML stack for autonomous vehicles: architect models for landmark-based localization across sensors, own data and evaluation pipelines, drive feature development to deployment, define failure modes and safety coverage, and mentor engineers while integrating cutting-edge multimodal and foundation-model techniques into robust, real-time localization systems.

Build end-to-end ML software for autonomy foundation models: data pipelines, training/evaluation workflows, model implementation (LLM/VLM/VLA), simulator integration, and low-latency serving and inference tooling for vehicle deployment.

The Machine Learning Researcher will build predictive models using deep learning, explore strategies to enhance portfolio performance, and update models based on research. They should have a strong foundation in ML with hands-on experience in relevant domains.

Build core, shared ML capabilities for a production platform: foundation models for documents, extraction agents, model routing, unified evaluation, and continuous improvement pipelines. Own end-to-end capability development, production deployment, and cross-team support to improve accuracy, latency, cost, and reliability across institutional workflows.

Build, train, fine-tune, evaluate, and deploy machine learning and LLM models for production. Create monitoring, evaluation, and feedback tooling, evaluate new techniques, and collaborate cross-functionally to ship ML-powered features.

The Founding ML Engineer will develop and deploy ML systems, collaborate with teams, and translate ambiguous problems into production ML solutions, shifting between client delivery and platform development.

Design, develop, implement, and monitor statistical and machine learning models to support credit risk, collections, servicing, marketing, and forecasting. Build data pipelines, translate business needs into analytical solutions, support model governance, testing, deployment, and ongoing performance monitoring, and communicate results to stakeholders.

Lead development and deployment of predictive and ML models for credit risk, collections, and operations. Mentor data scientists, promote MLOps and modern tools, present insights to stakeholders, and drive experimentation with techniques such as causal inference and GenAI.

As a Senior Machine Learning Engineer, you will own the ML stack, develop generation systems, improve quality, collaborate cross-functionally, and build data pipelines.

Partner with customers to design and deliver production-ready AI/ML solutions, drive POC-to-production success, troubleshoot technical issues, inform product roadmap with customer feedback, and communicate outcomes to stakeholders.

As a Senior Machine Learning Engineer, you will develop and deploy AI-driven products to enhance clinical trials, collaborating with teams to improve patient care through scalable machine learning solutions.

The Senior ML Engineer will architect data pipelines, develop classification algorithms, manage data integration, and mentor team members in a cross-functional setting.

Research and build efficient ML systems for large-scale LLMs and agentic RL: design algorithms and system techniques, prototype in training/inference stacks, run large-scale experiments, and translate findings into production or publications.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

As an ML Engineer at Luma AI, you will design high-performance model serving pipelines, manage GPU resources, and optimize CI/CD for large-scale AI systems.

Lead the Machine Learning strategy at Gather AI, focusing on computer vision, building teams, and enhancing ML systems for warehouse intelligence.

Design, build, deploy, and operate scalable production ML and AI systems (batch and real-time) to predict KPIs and optimize provider network operations. Own end-to-end model lifecycle including feature engineering, training, deployment, monitoring, CI/CD, and experiment tracking. Collaborate with product, engineering, clinical and business stakeholders, implement production-quality code (Python, Spark, SQL), and integrate LLMs, RAG, and other AI capabilities to deliver measurable business and clinical outcomes.

The Senior Machine Learning Engineer will develop end-to-end ML systems connecting perception inputs to driving behavior, enhance model performance, and mentor juniors.

The role involves designing scalable distributed systems using Scala, enhancing feature stores for performance, and collaborating with ML engineers to resolve challenges.

The Senior AI/ML Engineer will develop and improve analytical tools, approaches, and architecture for scalable AI and machine learning solutions, collaborating with engineers and data scientists on innovative projects.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.

Lead end-to-end development of fraud and risk ML systems: define problems, engineer features, build and evaluate models, collaborate with product and engineering, deploy with ML engineers, and monitor production performance to detect drift and trigger retraining.