Top Remote Machine Learning Engineer Jobs

Lead design, development, deployment, and operationalization of AI/ML solutions (including LLMs and Generative AI) for healthcare. Provide technical leadership on chatbots, conversational AI, and NLP, ensure responsible AI and production reliability, mentor team members, and partner with product and engineering to deliver scalable cloud-hosted solutions.

Lead development and production of underwriting and credit decisioning models across Cash App Borrow and Afterpay. Own full modeling lifecycle: problem formulation, feature engineering, training, calibration, experimentation, deployment, monitoring, and iteration. Build decision frameworks, agentic engineering workflows, and collaborate with cross-functional partners to align model behavior with business and regulatory goals.

Develop and deploy machine learning solutions for autonomous vehicles, mentoring engineers and collaborating on model implementations.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

Lead the design, implementation, and deployment of scalable ML infrastructure for autonomous driving. Drive technical projects, mentor others, and collaborate with teams to enhance ML workflows.

Lead and develop advanced machine learning models for autonomous driving, collaborating with teams, and driving technical initiatives in a hybrid work environment.

Senior individual contributor building and maintaining underwriting and credit decisioning ML systems for Cash App Borrow and Afterpay. Responsibilities include feature engineering, model training, calibration, experimentation, deployment, monitoring, and portfolio-level analysis. Collaborate with cross-functional teams to align models with business and regulatory goals and develop AI-native engineering workflows and governance for reliable, auditable model development.

Design, build, and deploy scalable AI/ML models and pipelines for consumer healthcare. Develop production code (Python/Java/Scala), automate infrastructure (Terraform, Kubernetes), validate model performance, integrate cloud services (Azure/AWS), and collaborate cross-functionally to monitor and document enterprise-scale AI solutions.

Lead the design and delivery of scalable voice and chat solutions using Amazon Connect, focusing on integrations, reliability, and performance optimization.

Build, ship, and operate end-to-end production AI/ML solutions for vehicle diagnostics, prognostics, and test analytics. Implement ML pipelines on Azure/Databricks, develop observable ML services, ensure model/data observability, and collaborate with SMEs to embed models into engineering workflows while mentoring other practitioners.

Develop and optimize machine learning models for data extraction from tax documents, collaborate on software integration, and mentor junior team members.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Develop large-scale Foundation Models for Autonomous Driving & Robotics, collaborating with teams, optimizing model performance, and mentoring junior members.

The Principal Machine Learning Engineer will lead ML initiatives, integrate ML services, mentor engineers, and drive ML strategy within Ibotta, ensuring scalable solutions are implemented effectively across teams.

Design, build, and deploy enterprise-scale generative AI and LLM applications, agentic workflows, and RAG pipelines. Develop full-stack solutions using React/Next.js, Python/Node.js, and PostgreSQL; integrate vector databases and optimize retrieval; lead technical design, mentor engineers, and ensure responsible AI deployment and scalability.

Lead design, development, deployment, and improvement of AI/ML solutions (including LLMs and Generative AI) for healthcare. Build responsible AI, chatbots, and NLP systems; ensure security, monitoring, and production operations; integrate AI with cloud modernization; provide technical leadership, mentorship, and cross-functional collaboration to meet business and regulatory requirements.

The Lead AI/ML Engineer will design and implement ML models, integrate AI solutions, and collaborate with technical teams to enhance AI capabilities, focusing on healthcare applications.

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 a global research organization to define scientific vision and deliver large-scale ML/optimization systems for prediction, bidding, targeting, and marketplace efficiency. Translate research into production, recruit and mentor scientists, set technical standards, partner with product and engineering, and drive measurable revenue and platform improvements through experimentation and optimization.

Lead a global research organization to define scientific vision and build large-scale ML and optimization systems for targeting, bidding, forecasting, and marketplace efficiency. Translate research (reinforcement learning, causal inference, auction design, operations research) into production, partner with Product and Engineering, recruit and mentor scientists, drive experimentation and measurable revenue/efficiency impact, and represent the company in publications and conferences.

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.

Lead the design, development, and deployment of AI and machine learning solutions, mentor teams, and drive innovations in healthcare data analysis.

As a Senior AI/ML Engineer, you will design and build end-to-end ML systems, manage ML lifecycle workflows, and ensure compliance with AI standards within the healthcare sector.

Design and implement AI/ML solutions for healthcare, collaborating with teams to translate complex requirements, deploy models, and process large datasets.