Senior AI Research Engineer

Role Overview

Join Phaidra as a Senior AI Research Engineer and help connect research to production. You’ll own and evolve the research infrastructure, build scalable distributed compute for research workloads, and improve R&D speed/quality through performance engineering.

Key Missions

• Own responsibility for research infrastructure and the orchestration of experiments through model production.
• Build and scale distributed compute for research workloads, including GPU capacity management.
• Improve R&D speed and quality using performance engineering to identify and resolve bottlenecks.
• Mentor peers and serve as a point of contact between research and production engineering.
• Participate in medium-to-long-term decisions that shape the vision for research at Phaidra.

Requirements

• 6+ years progressive relevant experience after Bachelor’s degree.
• 4+ years progressive relevant experience after Master’s degree.
• Strong engineering foundation with general scientific understanding (e.g., ML, optimization, control, or physical sciences).
• Demonstrated ability to lead projects and own delivery end-to-end.
• Solid understanding of lower-level programming languages such as C++ and Rust.
• Experience in ML R&D (Software Engineer or ML Engineer), ideally bridging research and production.
• Understanding of ML and ML-Ops; ability to reason about systems with non-deterministic components.
• Fluency in a high-level language, preferably Python.
• Solid grasp of ML/ML-Ops concepts such as experiment tracking, model registries/deployment, and deployment reasoning for non-deterministic systems.
• Exceptional organizational and communication skills.
• Strong alignment with values: transparency, collaboration, operational excellence, ownership, empathy.

Nice to Have

• Experience related to industrial heating/cooling processes (manufacturing or data center applications).
• Prior research experience in ML/AI/MLOps.
• Experience with Ray for distributed computing and multi-node GPU orchestration.
• Exposure to reinforcement learning, simulation, and control systems.
• A scientific/physical-science foundation to collaborate closely with researchers.

Location

• Full remote