Top Hybrid Machine Learning Engineer Jobs

Build and deploy ML and statistical models (recommendation, personalization, causal inference, GenAI) to improve eCommerce digital experiences. Partner with product, engineering, and business teams, own analytics projects end-to-end, and communicate insights to stakeholders.

Lead and grow a team of ML engineers building applications for a graphically intensive simulation platform. Manage stakeholders, translate customer needs into technical requirements, prioritize between off-the-shelf, open-source, and greenfield solutions, coordinate cross-functional delivery, and oversee the full ML lifecycle from data infrastructure to deployment. Foster team ways of working and performance.

Design, build, and deploy transformer-based ML and LLM systems to automate risk investigations and decisioning. Own end-to-end pipelines (data ingestion, prompt tuning, orchestration, productionization, monitoring) and collaborate with product, engineering, and operations to deliver copilot/autopilot workflows that improve analyst productivity and eliminate manual reviews.

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.

Lead experimental ML initiatives: design, train, and deploy multi-modal models and production pipelines; evaluate and prototype emerging ML technologies and agentic workflows; establish AI governance, mentoring, and knowledge sharing; translate research into business impact and thought leadership.

Analyze and evaluate machine learning algorithms, develop advanced algorithms for modeling, and collaboratively transition them to software applications, while engaging with stakeholders and researching innovative solutions.

Design, build, and deploy production-grade AI applications using LLMs, RAG, and agent architectures. Implement vector-database-backed retrieval solutions, create scalable REST APIs and cloud-native backends, and collaborate with cross-functional teams to translate business requirements into robust AI solutions following engineering best practices.

The role involves developing control algorithms for complex robots, integrating control with learning components, and ensuring system safety under real-time constraints.

Lead development of predictive and prescriptive supply chain models using statistics, machine learning, and optimization. Partner with IT, product managers, and engineers to scale analytics products, design experiments, run simulations, mentor analysts, and translate insights into operational improvements.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.