SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Phaidra is building AI-powered control systems for industrial automation, using reinforcement learning to enable factories, power plants, and buildings to automatically learn and improve over time. The company converts raw sensor data into high-value actions, allowing domain experts to configure AI agents without coding.
As a Senior AI Research Engineer, you will own and evolve the research infrastructure end-to-end, spanning the full lifecycle from experiment orchestration and distributed training to model tracking, evaluation, and automated deployment. You'll build and scale distributed compute for research workloads using Ray-based training and data pipelines on Kubernetes/GCP, managing GPU capacity across zones and regions while maintaining reliability and cost efficiency.
Key responsibilities include: improving R&D speed and quality through performance engineering (vectorizing and parallelizing simulators, profiling bottlenecks, driving large speedups); deeply understanding Phaidra's internal platform and how to leverage it for customers; maintaining clear documentation; participating in medium-to-long-term strategic decisions; mentoring peers and delegating tasks while owning project delivery; and acting as a bridge between Research and Production engineering teams to rapidly productionize breakthroughs.
You'll wear multiple hats as ML Engineer, ML-Ops Engineer, Software Engineer, and Performance Engineer. The role requires 4+ years of progressive experience, with a track record of leading projects and owning end-to-end delivery. You should have prior experience as a Software or Machine Learning Engineer in an ML R&D environment, ideally bridging research and production. Strong proficiency in Python is essential, with solid understanding of lower-level languages like C++ and Rust. You'll need deep knowledge of ML and ML-Ops concepts (experiment tracking, model registries/deployment, reasoning about non-deterministic systems), paired with a strong engineering profile and scientific understanding (ML, optimization, control, or physical sciences). Natural curiosity, desire to learn deeply, and excitement about real-world impact are essential.
