Senior AI Research Engineer

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

• Either:
  • 4+ years of progressive relevant work experience after Master’s graduation.
  • 6+ years of progressive relevant work experience after Bachelor’s graduation.
• 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 domains.
• Excited to see the real world impact of your work; celebrate the success of your customers as your own.
• Exceptional organizational and communications skills.
• Alignment with Phaidra’s values: transparency, collaboration, operational excellence, ownership, empathy.

Preferred Skills & Experience

• Understanding of industrial heating and cooling processes and their applications within manufacturing or data center environments.
• Previous research experience in the field of ML or AI or MLOps experience.
• Experience with Ray for distributed computing and orchestrating multi-node GPU workloads.
• Exposure to reinforcement learning, simulation, and control systems.
• A general scientific or physical-sciences foundation that helps you collaborate closely with researchers.

Our Stack 

• Python
• PyTorch, scipy, scikit-learn, numpy, pandas, MLflow
• Docker, Kubernetes, Ray
• GCP

Onboarding 

In your first 30 days…

• You will be immersed in an onboarding program that introduces you to Phaidra, our product and our remote working norms. Your onboarding buddy will be there every step of the way.
• You will read various parts of our handbook and familiarize yourself with the documentation culture at Phaidra.
• You will set up your development environment and start working on an onboarding exercise that will introduce you to various parts of our code base.
• You will learn about various team standards and development & release processes.
• You will start to learn about our system architecture and infrastructure.
• You will familiarize yourself with the tools used to manage customer onboarding and operations.

By your first 60 days…

• You will have a solid understanding of what Phaidra does and how we do it.
• You will have met with team members across Phaidra and started building relationships that will help you be successful at your job.
• You will have started a project to improve Phaidra’s R&D tooling and/or infrastructure

By your first 90 days…

• You will have been fully integrated in the team and with team members across the company.
• You will have acquired a more in-depth understanding of our system architecture and infrastructure.
• You will have identified process or tooling improvements and started bringing people together to work on solutions.
• You will have become an expert with our systems. You will start to manage and own our tooling and infrastructure, seeing it accelerate our R&D efforts.
• You will have started to contribute to knowledge sharing throughout Phaidra.
• You will have delighted customers!

General Interview Process

All of our interviews are held via Google Meet, and an active camera connection is required.<