Top Hybrid Machine Learning Engineer Jobs

Develop and implement machine learning solutions using LLMs and the LangChain framework. Ensure production quality code and utilize various ML techniques and containerization tools.

Design, build, and deploy production-grade AI/ML models, agentic workflows, and data pipelines using Python, Databricks, LangChain/LangGraph, and Azure. Collaborate with cross-functional teams to translate requirements into scalable LLM applications, implement feature engineering and validation frameworks, and embed responsible AI practices including fairness, security, and monitoring.

Lead model risk management for Card Intelligence: develop and validate machine learning models end-to-end, define model development standards and change management, collaborate with cross-functional teams, and translate technical results into business goals while using cloud and open-source tools at scale.

The role involves building customer valuation models, enhancing marketing efficiency via intelligent targeting, and implementing machine learning solutions for the Retail Bank.

In this role, you will lead AI projects, collaborating with cross-functional teams to develop and deploy advanced AI solutions, focusing on LLM inference and model optimization.

Lead design and delivery of agentic, LLM-powered backend and platform AI systems. Build and operate Java and Python microservices, implement orchestration, RAG, prompt management, observability, and cost telemetry. Drive architecture, integration with platform and vendor components, enforce engineering standards, and ensure Responsible AI, security, and production reliability at enterprise scale.

Lead Gusto's AI/ML strategy, managing teams in Machine Learning Engineering, AI, and Risk Data Science. Ensure systems are reliable and impactful while fostering a culture of technical excellence.

As a Machine Learning Engineer in Fraud Risk, you'll build and maintain ML systems for fraud detection and anomaly analysis, ensuring robust performance and real-time decision making.

Lead design and implementation of complex embedded and application software for signal processing and algorithm development (including machine learning) for EO/IR and electronic warfare systems. Provide technical leadership, mentor teams, produce design/project documentation, evaluate and optimize algorithms (C++), and collaborate with systems, hardware, and test engineers through full lifecycle development and customer technical reviews.

Design, prototype, and productionize applied AI/ML solutions for simulation and engineering workflows. Build high-performance inference pipelines, fine-tune and optimize multimodal and 3D models, integrate models into simulation or perception stacks, and lead projects end-to-end while collaborating with simulation, data, and engineering teams.

Build and ship full‑stack web features and microservices for a cloud‑native genomics platform. Lead design and code reviews, mentor engineers, and develop production LLM applications (RAG, evaluation, citations) using managed endpoints and containerized CI/CD deployments. Collaborate with Product, QA, and Architecture to deliver reliable, regulated healthcare software.

Lead end-to-end machine learning projects to improve customer engagement and retention. Build, test, and deploy models for user forecasting, personalization, and Casino engagement. Partner cross-functionally to scale solutions, mentor junior data scientists, and communicate model impact to influence product and marketing strategy.

Develop, implement, and document advanced statistical, econometric, and machine learning models to assess and manage credit risk. Collaborate with business partners, auditors, regulators, and technical teams, working with large-scale data and distributed processing to support credit decisioning and portfolio management.

Develop, implement, and document advanced statistical, econometric, and machine learning models for credit risk. Collaborate with business partners, auditors, and regulators, work with large-scale data and distributed processing, and support credit decisioning, portfolio management, and model governance.

Develop, implement, and document advanced statistical, econometric, and machine learning models to evaluate and manage credit risk. Analyze large-scale data, collaborate with business partners, auditors and regulators, and support credit decisioning, portfolio management, and strategy.