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 ROLEOwn 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 WILLDesign & 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 HAVETrack 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±1) and able to travel occasionally to customer warehouses.We are not considering entry-level or coursework-only profiles for this role.HIRING PROCESS30-min intro & mutual fitDeep 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