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.



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

HIRING PROCESS

1. 30-min intro & mutual fit
2. Deep technical session with CTO on your past RL work (no LeetCode, no homework)
3. Two one-hour “Traits & Skills” conversations with our other Co-founders.
4. Meet the team & offer