Top Remote Machine Learning Engineer Jobs

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.

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

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.

Drive technical strategy for the Personalization Platform, develop recommendation systems, maintain ML workflows, and innovate AI solutions for customer engagement.

As a Principal Software Engineer, you'll architect and code backend services and ML models, integrate them with Salesforce, and mentor junior engineers.

Lead and mentor multiple engineering teams to deliver AI-enabled MSK platform solutions. Drive AI strategy, design and deploy responsible ML solutions, ensure regulatory (CMS) compliance, oversee vendor partners, modernize platform architecture, and manage production support and incident governance.

Lead the design and development of AI/ML models for healthcare. Collaborate with business partners and other teams to ensure high-quality execution and adherence to architectural principles.

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

The Staff Machine Learning Engineer will lead ML initiatives, partnering with multiple teams to enhance customer support through AI-driven solutions, focusing on conversational agents and research in chatbot architectures.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

As a Senior AI/ML Engineer, you'll enhance developer productivity by leading CI solutions, mentoring engineers, and implementing scalable systems.

Lead design and delivery of enterprise AI/ML and intelligent automation platforms. Define architecture strategy, build backend APIs and event-driven pipelines, deploy cloud-native solutions, drive adoption of generative and agentic AI, mentor engineering teams, and ensure security, scalability, and governance across AI initiatives.

The Senior AI/ML Engineer will enhance developer productivity by improving Python workflows, leading projects, mentoring engineers, and driving best practices.

Lead design and deployment of production-grade ML systems (ranking, retrieval, LLMs). Drive architecture decisions, run experiments and evaluations, build scalable feature pipelines and monitoring, mentor engineers, and collaborate cross-functionally to integrate AI into products and scale ML capabilities.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

The Staff Machine Learning Engineer will lead the development of AI solutions, ensure project delivery, and manage technical teams while interfacing with customers. Responsibilities include designing data pipelines, overseeing machine learning model development, and facilitating customer engagement.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.

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.

Build and productionize statistical and ML models to optimize advertiser performance and business goals in mobile ad tech. Tune optimization, model competition and dynamic environments, design experiments, develop budget retention/growth and bidding systems, and collaborate across engineering and business teams while ensuring explainable, well-tested solutions.

Design, build, and deploy production-grade ML and AI solutions for behavioral health. Architect end-to-end MLOps pipelines, optimize and monitor models in production, evaluate emerging AI trends, collaborate cross-functionally, and provide technical leadership and mentorship to engineering teams while ensuring responsible, scalable, and secure AI deployments.

Lead cloud migrations for voice contact center solutions, design AI/ML capabilities, improve automation and governance, and ensure production readiness while collaborating with cross-functional teams.

Lead design, development, and production deployment of AI/ML solutions and conversational chatbot platforms. Own end-to-end implementation, MLOps/CI-CD, containerized deployments, solution architecture, security and governance, mentor engineers, and collaborate with product, enterprise architecture, and vendors to deliver scalable, monitored AI systems.

Lead technical design and delivery of production AI/ML and GenAI solutions (RAG, LLMs, agents). Build, tune, deploy, and monitor models and APIs, own MLOps practices, operate cloud AI workloads, ensure software quality and responsible AI, and partner with cross-functional stakeholders while mentoring engineers.

Design, build, test, and deploy production AI/ML solutions and full‑stack applications (React/Next.js, Python, PostgreSQL). Integrate LLMs and agentic AI frameworks, develop backend services/APIs, collaborate cross-functionally, and optimize reliability and performance within regulated clinical workflows.

Lead design, development, and deployment of production-grade, large-scale ML systems. Influence ML strategy, integrate models with platform and data infrastructure, mentor engineers, communicate results to stakeholders, and mature ML infrastructure and abstractions across teams.