Deep Reinforcement Learning Engineer (Principal)

Friday Systems; Badajoz; A distancia; EUR 70.000 - 100.000; Jornada completa

Descripción de la vacante

A cutting-edge AI company in Badajoz, Spain, is seeking a senior AI engineer to own and implement deep reinforcement learning (DRL) algorithms. This role involves collaboration with the CTO and leading a high-performing team focused on delivering innovative AI solutions. Candidates should have extensive experience in deep learning, reinforcement learning, and productionizing algorithms within a team environment.

Servicios

Salary + equity

Flexible working conditions

Direct impact on technology development

Formación

• Proven experience shipping reinforcement learning systems from concept to production.
• Strong understanding of DRL algorithms and deep learning frameworks.
• Comfortable in a hands-on coding environment with a focus on results.

Responsabilidades

• Design and implement DRL algorithms for industrial applications.
• Ensure product excellence and align technology solutions with client needs.
• Lead and mentor a high-performing AI team.

Conocimientos

Deep Learning

Reinforcement Learning

PyTorch

Python

Docker

AWS

Herramientas

Multi-GPU systems

Cloud technologies

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