AI Research Engineer

Summary

Join Phaidra, a company building AI-powered control systems for industrial automation, as a Machine Learning Engineer. You will be part of a research team focused on applying AI to real-world industrial problems. The role involves building robust ML pipelines, collaborating with researchers and engineers, and deploying intelligent systems. You will work on cutting-edge methods in Deep Learning and Reinforcement Learning, integrating models into real-time systems, and performing rigorous evaluations. The position is fully remote, with opportunities for significant impact and professional development. Phaidra values collaboration, transparency, operational excellence, ownership, and empathy. The ideal candidate has 1+ years of experience in applied machine learning or software engineering and a relevant degree.

Requirements

• 1+ years of prior experience in applied machine learning or software engineering
• Bachelor’s or Master’s degree in Computer Science, Engineering, or related technical field
• Proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or JAX
• Strong foundations in supervised learning, reinforcement learning, or control systems
• Familiarity with common tools for experimentation (e.g., Ray, MLFlow, WandB, or similar)
• Ability to write high-quality, testable, and maintainable code
• Share our company values: Collaboration, Transparency, Operational Excellence, Ownership, and Empathy

Responsibilities

• Building robust ML pipelines for training, evaluation, and deployment of control algorithms
• Working with researchers to implement cutting-edge methods in Deep Learning and Reinforcement Learning
• Collaborating with software engineers to integrate models into real-time systems
• Designing tools and infrastructure to support large-scale experimentation and iteration
• Performing rigorous evaluations on simulation and real-world data, and tuning models for safety, performance, and robustness
• Supporting reproducibility and code quality across the team

Preferred Qualifications

• Experience with Deep RL methods (e.g., PPO, SAC, DDPG, Model-Based RL)
• Familiarity with numerical optimization and physical modeling
• Experience building infrastructure for experimentation, model serving, or data processing
• Understanding of industrial systems and control theory is a strong plus
• Exposure to distributed computing, cloud infrastructure (GCP, AWS), or containerization tools (Docker, Kubernetes)

Benefits

• Competitive compensation & equity
• Outsized responsibilities & professional development
• Training is foundational; functional, customer immersion, and development training
• Medical, dental, and vision insurance (exact benefits vary by region)
• Unlimited paid time off, with a minimum of 20 days off per year requirement
• Paid parental leave (exact benefits vary by region)
• Home office setup allowance, coworking space stipend, and company MacBook