Deep Reinforcement Learning Engineer (Principal)

Friday Systems builds AI that allows industrial robots to adapt to dynamic warehouse environments. We focus on high-throughput palletizing and related tasks where classical approaches break down. Our stack is built around Deep Reinforcement Learning with modern sequence models., Own the DRL stack end-to-end : formulation * algorithm design * large-scale training * evaluation * deployment. You'll work directly with the CTO to turn cutting-edge DRL into production throughput at customer sites. YOU WILL

• Design & ship DRL algorithms (PPO / SAC / DDQN and variants, based on encoders / cross-attention / pointer networks) for complex control & combinatorial optimization.
• Tackle stability & sample-efficiency : GAE, normalization, entropy / KL control, distributional / value-loss tuning, curriculum learning and reward shaping, …
• Launch multi-GPU training, parallel rollouts, efficient replay / storage, and reproducible experiment tooling.
• Productionize : clean PyTorch code, profiling, Dockerized services (FastAPI), AWS deployments, experiment tracking, dashboards.
• Collaborate with the C-Level Team to ensure product excellence and alignment with business strategy. Forge strong relationships with clients, effectively translating their needs into unique technology solutions.
• Build and nurture a high-performing team by attracting top talent. Provide mentorship and leadership to foster a culture of quality and innovation.

• Track record shipping RL beyond academic demos : you've led at least one end-to-end RL system from idea to production or a state-of-the-art benchmark in the last 3-5 years.
• Extensive Deep Learning, Reinforcement Learning & PyTorch expertise : You can implement several DRL algorithms from scratch, reason about root-cause performance drops and make informed decisions about next steps.
• Systems know-how : Python, Linux, Docker, Multi-GPU, Cloud (AWS).
• Math maturity : MDPs / Bellman operators, policy gradients, trust-region / KL, GAE / λ-returns, stability / regularization in on-policy vs off-policy regimes.
• Ownership : you're comfortable being the primary owner for experiments, code quality, and results in a small team.
• Location / time zone : EU-based (CET±2) and able to travel occasionally to customer warehouses.