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.

Tiny team, zero bureaucracy, direct impact, salary + equity.

THE ROLE

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.

YOU HAVE

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.

We are not considering entry-level or coursework-only profiles for this role.

HIRING PROCESS

30-min intro & mutual fit

Deep technical session with CTO on your past RL work (no LeetCode, no homework)

Two one-hour “Traits & Skills” conversations with our other Co-founders.

Meet the team & offer