About Phaidra

Phaidra is building the future of industrial automation.

The world today is filled with static, monolithic infrastructure. Factories, power plants, buildings, etc. operate the same they've operated for decades — because the controls programming is hard-coded. Thousands of lines of rules and heuristics that define how the machines interact with each other. The result of all this hard-coding is that facilities are frozen in time, unable to adapt to their environment while their performance slowly degrades.

Phaidra creates AI-powered control systems for the industrial sector, enabling industrial facilities to automatically learn and improve over time. Specifically:

We use reinforcement learning algorithms to provide this intelligence, converting raw sensor data into high-value actions and decisions.
We focus on industrial applications, which tend to be well-sensorized with measurable KPIs — perfect for reinforcement learning.
We enable domain experts (our users) to configure the AI control systems (i.e. agents) without writing code. They define what they want their AI agents to do, and we do it for them.

Our team has a track record of applying AI to some of the toughest problems. From achieving superhuman performance with DeepMind's AlphaGo, to reducing the energy required to cool Google's Data Centers by 40%, we deeply understand AI and how to apply it in production for massive impact.

Phaidra’s ability to achieve its mission is determined by our ability to work together — as defined by our core values: Transparency, Collaboration, Operational Excellence, Ownership, and Empathy. We seek individuals who embody these values, as they are instrumental in ensuring our team consistently delivers excellence and fosters an engaging and supportive culture

Phaidra is based in the USA, but we are 100% remote with no physical office. We hire employees internationally with the help of our partner, OysterHR. Our team is currently located throughout the USA, Canada, UK, Sweden, Spain, Portugal, the Netherlands, Singapore, Australia, and India.

Responsibilities

Wear different hats across the research-to-production lifecycle: ML Engineer, ML-Ops Engineer, Software Engineer, and Performance Engineer.
Own and evolve our research infrastructure end-to-end, from experiment orchestration and distributed training to model tracking, evaluation, and automated deployment, so researchers can move from idea to validated result quickly.
Build and scale distributed compute for research workloads (e.g. Ray-based training and data pipelines on Kubernetes/GCP), including managing GPU capacity across zones/regions and keeping experiment infrastructure reliable and cost-efficient.
Improve the speed and quality of our R&D through performance engineering: vectorizing and parallelizing simulators and training code, profiling bottlenecks, and driving large speedups.
Deeply understand the capabilities and tools offered by Phaidra’s internal platform and how to utilize them to best serve our customers.
Maintain clear and concise documentation of your research, products and actions.
Participate in making decisions for the medium-to-long-term vision impacting Research and Phaidra.
Mentor peers and delegate tasks within the team, owning the project delivery.
Act as a point of contact between Research and Production engineering teams to productionize new breakthroughs rapidly.

Key Qualifications

4+ years of progressive relevant work experience.
Previous experience in leading projects and owning delivery end-to-end.
Previous experience as a Software Engineer or Machine Learning Engineer in an ML R&D environment, ideally bridging research and production.
Understanding of ML and ML-Ops concepts and ability to reason about systems with non-deterministic components.
Solid grasp of ML and ML-Ops concepts — experiment tracking, model registries/deployment, and the ability to reason about systems with non-deterministic components.
Fluency in a high level programming language, preferably Python.
A solid understanding of lower level programming languages such as C++ and Rust.
A strong engineering profile paired with a general scientific understanding (e.g. ML, optimization, control, or the physical sciences), so you can grow alongside our rapidly changing priorities and quickly go deep in unfamiliar domains.
Natural curiosity; desire to learn, go deep and problem-solve in new do

Senior AI Research Engineer at Phaidra