Top Remote Machine Learning Engineer Jobs

Customer-facing, hands-on AI builder shaping opportunities, prototyping AI/GenAI solutions, designing end-to-end architectures, leading discovery workshops, supporting RFPs, and enabling implementation handoffs while creating reusable AI assets and accelerators.

Customer-facing, hands-on AI builder who shapes strategic pursuits, designs end-to-end AI/GenAI architectures, prototypes demos and proofs-of-value, mentors solution leads, supports RFPs, and partners with engineering for implementation and delivery rotations.

Design, build, and deploy generative AI solutions (LLMs, RAG, Agentic AI) for the Pharmacy domain. Collaborate with pharmacists and capability teams, containerize projects, implement CI/CD, validate and optimize models, maintain code quality and security, and support production environments on cloud platforms while working Eastern Time hours and participating in on-call rotations.

The Machine Learning Engineer develops production ML algorithms and recommendation systems, collaborates with teams, and optimizes user experiences.

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 design and delivery of scalable Amazon Connect voice and chat solutions. Build Lambda-based integrations, integrate Lex bots and external systems, optimize routing/queues, ensure high availability and monitoring, lead production support, RCA, and continuous improvement for contact center platforms.

Lead design, development, deployment, and maintenance of AI/ML solutions (LLMs, generative models, predictive analytics) and conversational agents. Integrate AI features with web/APIs and cloud infrastructure, ensure responsible AI, security, and operational excellence, mentor team members, and partner with product and architecture to deliver scalable healthcare solutions.

Lead design, development, deployment, and maintenance of responsible AI/ML solutions (including LLMs, generative models, chatbots, and agent orchestration) for provider-facing products. Provide technical leadership, mentor engineers, align solutions with enterprise AI strategy and compliance, integrate AI into cloud architectures, and ensure production reliability and operational excellence.

Design, build, and deploy end-to-end scalable AI/ML solutions (classical ML, NLP, generative AI, RAG). Implement MLOps/CI-CD, productionize models, monitor performance, mentor engineers, and align solutions with business objectives while applying Responsible AI practices.

Lead technical direction and people for an AI/ML engineering squad to design, build, and deploy production-grade ML and Generative AI (LLMs, RAG, agentic systems). Drive MLOps, CI/CD, model monitoring, Responsible AI compliance (HIPAA/PHI), architecture reviews, and mentor 5–10 engineers to deliver secure, production solutions.

Lead design, build, and operate end-to-end AI/ML-enabled full-stack applications for clinical workflows. Develop Next.js/React frontends, Node.js backends, PostgreSQL data models, integrate LLM capabilities, deploy and monitor on Azure, implement CI/CD and security best practices, and mentor engineers.

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.

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

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

The Senior AI ML Engineer will develop AI systems, ensure model quality, lead architecture governance, and manage observability for healthcare solutions.

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.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

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

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

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.

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.

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.