Deep Reinforcement Learning Engineer (Principal)

Friday Systems; Santander; A distancia; EUR 80.000 - 120.000; Jornada completa

Descripción de la vacante

A forward-thinking AI company is seeking a skilled individual to own and optimize their Deep Reinforcement Learning stack. The ideal candidate will have extensive experience in shipping RL systems, extensive knowledge of algorithms, and will be capable of collaborating closely with C-level executives to deliver performance-driven solutions. This role is based in the EU and requires occasional travel to customer sites.

Servicios

Salary + equity

Direct impact in a small team

Flexible working hours

Formación

• 5+ years leading RL systems from concept to production or achieving SOTA benchmarks.
• Deep knowledge of DRL algorithms and root-cause analysis for performance issues.
• Experience with systems including Python, Linux, Docker, and Multi-GPU setups.

Responsabilidades

• Design and ship DRL algorithms for control and optimization.
• Ensure stability and sample efficiency across multiple training strategies.
• Productionize clean, efficient PyTorch code and develop Dockerized services.

Conocimientos

Deep Learning

Reinforcement Learning

PyTorch

Python

Docker

AWS

Descripción del empleo

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