Top Hybrid Machine Learning Engineer Jobs

Contribute to machine learning model design, training, and evaluation for fraud detection. Communicate findings, develop metrics, and support production ML workflows while ensuring compliance and model governance.

Design and maintain data pipelines and BI infrastructure, ensure data quality, optimize SQL and transformations, build and validate predictive and ML models, support deployment and BI integration, and collaborate with stakeholders to deliver scalable analytics solutions.

Lead modelling and analysis roadmap for FX products: forecast and monitor FX volatility, optimise trade execution with statistical/ML models, run customer and profitability analyses, and partner with product, trading, risk, and engineering to translate insights into trading and product decisions.

The Principal Engineer, AI/ML Architecture will define the technical direction for Coupa's AI platform, focusing on model training, evaluation, safety standards, and mentoring a team of ML and data engineers.

Design, build, and operate AI platform and orchestration systems for computer vision, perception, simulation, and mapping. Own pipelines, artifact handoffs, workflow control, deployment, observability, and APIs; enable reproducibility, tracking, and scalable production inference while partnering with data scientists to reduce iteration friction.

Serve as the primary technical owner for customer engagements, design and build integrations, write production-quality code, troubleshoot and ship scalable production solutions, capture precise product feedback, and partner with product and engineering to convert field learnings into roadmap improvements.

Design and build a multi-agent LLM framework and production LLM features (RAG, text-to-SQL, retrieval, prompts). Own architecture, testing, monitoring, and optimization for fleet/telemetry data in a fast-moving startup.

Build and scale distributed infrastructure for large-scale AI model training across GPU clusters. Responsibilities include improving reliability, fault tolerance, checkpointing, recovery, resource utilization, training pipelines, developer tooling, and operational processes. The role partners with platform, orchestration, performance, and machine learning teams to diagnose training issues and support production-ready AI workloads.

Build and scale LLM-based, agentic customer-engagement systems: architect, deploy, and monitor production ML features; finetune models; create evaluation frameworks, feedback loops and HITL workflows; run A/B tests; optimize latency, cost, and reliability; and mentor the team.

Build, train, evaluate, and deploy production machine learning and LLM systems for legal document intelligence. Develop NLP solutions for information extraction, summarization, search, AI agents, and document analysis. Create scalable ML pipelines, evaluation frameworks, and reliability metrics while optimizing model performance. Collaborate with engineering, product, legal, data, and security teams to deliver secure AI capabilities and translate business problems into production solutions.