Top Remote Machine Learning Engineer Jobs

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, development, and deployment of large-scale production ML systems and platform strategy. Research and prototype ML and big-data technologies, integrate ML services across the org, mentor engineers and data scientists, and communicate complex ML solutions to technical and non-technical stakeholders.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.

Design, build, and deploy LLM-based ML solutions using LangChain, fine-tuning, and RAG. Write production-quality Python code (PEP8), containerize with Docker, use Git, test and document work, and apply traditional ML methods like XGBoost and K-means.

Design, build, and deploy production AI/ML and generative-AI solutions (including RAG and agentic workflows) to enhance sales and distribution platforms. Develop scalable components, API integrations, microservices, and automation pipelines; monitor model performance; ensure Responsible AI, security, and enterprise governance; and collaborate with cross-functional and offshore teams to deliver reliable, high-performing systems.

Technical lead for end-to-end ML and CV pipelines to reconstruct, validate, and maintain map primitives from multi-modal vehicle sensor data. Architect scalable offline and production map reconstruction systems, define technical strategy, drive cross-functional integration, mentor senior engineers, and ensure robustness, safety, and operational scalability for national-scale mapping.

Design, train, and productionize underwriting ML models for real-time transaction decisions. Build feature pipelines and training datasets, run experiments, integrate models into batch/real-time systems, and monitor data and model health while collaborating with cross-functional teams.

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.

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.

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

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

Design, build, evaluate, and deploy AI/LLM-powered solutions (RAG, agents, classifiers, summarizers) for consumer-facing products. Develop prompt strategies, guardrails, and evaluation frameworks; partner with data engineering and cross-functional teams to ensure trusted data, observability, safety, and continuous improvement of model performance in production.

Design, build, and deploy production-grade AI/ML and LLM solutions for claims processing, eligibility, prior authorization, and payment integrity. Build scalable data pipelines, integrate models with core payer systems, ensure HIPAA/PHI-safe pipelines and Responsible AI governance, monitor and retrain models, and partner with cross-functional teams to deliver reliable, CI/CD-driven solutions.

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.

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.

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.

Design, develop, and deploy agentic AI workflows and LLM-based automations for clinical and business use. Build scalable RAG solutions, vector DB integrations, and production-grade Python services. Implement AI orchestration frameworks, ensure MLOps/LLMOps, monitor observability, and collaborate with product, clinical, security, and engineering teams to deliver secure, compliant healthcare AI capabilities.

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

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

Lead end-to-end design, implementation, deployment, and operation of production AI/ML-enabled full-stack applications. Build LLM-powered features, design scalable backend services and data models, deploy on Azure, implement CI/CD and observability, and mentor engineers while ensuring secure, compliant integration into clinical workflows.

Lead technical direction and people management for an AI/ML engineering squad. Design, build, and deploy production-grade ML and Generative AI solutions (LLMs, RAG, agentic systems), drive MLOps/CI-CD, ensure Responsible AI and HIPAA-compliant practices, conduct architecture/code reviews, and mentor engineers to deliver measurable business outcomes.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, identity abuse, merchant/marketplace risk, scams, and other adversarial activity. Own end-to-end production lifecycle: data contracts, low-latency inference, batch scoring, feature quality, deployment, monitoring, incident response, rollback, and feedback loops. Develop AI-assisted workflows and reusable decision capabilities while partnering with modelers, product, compliance, and operations.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.