Top Hybrid Machine Learning Engineer Jobs

Implement and productionize ML research into performant C++ and Python systems, build ML infrastructure and training pipelines, design LLM/agent applications, and deploy AI workloads across edge, near-edge, and cloud environments while collaborating with data science and engineering teams.

Lead product strategy for AI-powered personalization and recommendation capabilities. Define requirements, partner with Data Science and Engineering to build, test, launch and optimize models, monitor performance, run experiments, prioritize enhancements using analytics, and communicate results to stakeholders while ensuring scalability, compliance and strong customer experiences.

As a Staff Machine Learning Engineer, you will develop models and agents for AI integration into products, focusing on performance monitoring and issue resolution.

The Algorithmic Trading Senior Manager at FanDuel will lead the algorithmic trading team, focusing on developing automated trading strategies and optimizing capital utilization while managing risk and team operations.

The Machine Learning Engineer will design and implement scalable ML pipelines, work with 3D data, and collaborate directly with clients on engineering challenges, ensuring solutions are effective and impactful.

As a Machine Learning Engineer, you will design and deploy ML models, integrating LLMs with cultural intelligence and developing recommendation systems, while collaborating with data engineers and optimizing model performance.

The Research Engineer will develop scalable infrastructure for machine learning research, optimize workflows, and enhance model inference for trading systems.

Optimize deep learning networks for autonomous vehicles and large-scale data centers. Analyze and troubleshoot performance using profiling and roofline modeling, improve software architecture, latency, GPU memory usage, and throughput, and deploy scalable models with frameworks such as PyTorch or TensorFlow. Collaborate across engineering and research teams on large codebases, applying CUDA, C++, Python, and inference acceleration technologies in Linux/Unix environments.

Build, deploy, monitor, and retrain production machine learning models for search ranking, relevance, retrieval, content extraction, classification, and LLM-driven features. Develop large-scale data pipelines from crawl and query data, analyze experiments and behavioral logs, create A/B testing and offline evaluation frameworks, and guide product launch decisions using measurable results.

Lead end-to-end development and productionization of machine learning systems for causal marketing measurement and optimization, especially cMMM. Build probabilistic and statistical libraries, maintain ML pipelines, implement agentic AI workflows and model validation, review designs and code, collaborate cross-functionally, and mentor ML engineers.