Top Hybrid Machine Learning Engineer Jobs

Design, develop, and deploy machine learning models for ad targeting, ranking, and bidding optimization. Build personalization and recommendation systems at scale. Lead incrementality testing, attribution, and causal inference efforts. Partner with product, engineering, and analytics to deliver low-latency, production-ready ML pipelines and drive experimentation and measurement strategies across the AdTech ecosystem.

Design, build, and deploy ML models for ad targeting, ranking, and bidding optimization. Develop large-scale personalization and recommendation systems, attribution and incrementality testing frameworks, and experiment strategies. Partner with product, engineering, and analytics to productionize low-latency models, optimize performance and scalability, and apply causal inference and advanced measurement across the AdTech ecosystem.

Build, deploy, and monitor machine learning and pattern-recognition models to detect lending fraud. Analyze large transactional datasets with Python and SQL, partner with Fraud Operations to iterate on features and reduce false positives/negatives, communicate findings to stakeholders, apply AI in production, and mentor team members.

Develop applied AI, machine learning, optimization, and statistical models to solve complex business problems for Chase Finance and Business Management. Analyze large datasets, identify actionable insights, contribute throughout the modeling lifecycle, and communicate findings to senior stakeholders. The role also involves serving as a subject matter expert, advising partners, and introducing innovative analytical approaches.

Build and maintain scalable machine learning infrastructure, including distributed GPU training pipelines, model promotion workflows, monitoring integrations, and ML frameworks. Optimize training throughput, support model deployment and observability, and collaborate with product, architecture, data science, and engineering teams. The role requires strong Python, cloud, ML framework, big data, training and inference system, and enterprise design experience, with preferred expertise in recommendation systems, CUDA, containers, orchestration, and data storage.

Design, build, and operate scalable backend and ML-enabled services for Expedia's advertiser platform. Productionize ML/LLM features (recommendations, ranking, forecasting, RAG), own APIs, data/feature pipelines, monitoring, and lead cross-functional delivery and engineering best practices.

Design, build, and operate production-grade agentic AI systems for ServiceNow. Responsibilities include multi-agent orchestration, tool use, planning, memory, failure recovery, enterprise-grounded reasoning, RAG and search pipelines, safety guardrails, observability, model integration, evaluation frameworks, and cost and latency optimization. The role also provides technical leadership through architecture decisions, code reviews, and coaching.

Build and productionize scalable ML infrastructure for training, evaluating, and serving large language models. Develop distributed training and inference pipelines, optimize LLM latency, create automation abstractions, and collaborate cross-functionally to scale ML systems and best practices.

Leads the design, development, deployment, and maintenance of machine learning systems at scale. Builds ML models, data pipelines, cloud architectures, and production applications; applies model tuning, validation, monitoring, governance, responsible AI, CI/CD, and test automation. Collaborates with Product and Data Science teams in Agile environments, reviews code, optimizes performance, and helps lead teams delivering resilient ML solutions.

Lead research and development of emerging ML (embeddings, foundation models, transformers). Build and productionize machine learning models at scale, partner with product and engineering, analyze large-scale transactional and clickstream data, and run experiments to improve customer experiences across marketing, servicing, and fraud domains.

Lead development and deployment of ML models for trading, drive research and prototyping, oversee data and feature engineering, mentor researchers, and build scalable ML research infrastructure.

Own and architect the core ranking system end-to-end: model design, production reliability, automated retraining, bias mitigation, personalization, and cross-functional technical leadership. Mentor engineers, drive roadmap, and scale experimentation and ML infrastructure.

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.

As a Staff Machine Learning Engineer, lead teams in designing AI solutions, managing projects, and interfacing with clients while ensuring technical excellence and operational effectiveness.