Machine Learning Engineer — AI Architecture Research

Featherless AI; Poland; On-site; PLN 80,000 - 120,000; Full time

Benefits offered by this job

Competitive compensation

Meaningful equity

Direct influence on technical direction

Job summary

A Series-A technology company in Poland is seeking a Machine Learning Engineer focused on AI architecture research. The ideal candidate will design, prototype, and validate new neural network architectures while collaborating with engineers to ensure deployable and efficient models. Strong fundamentals in machine learning, hands-on experience in model architecture implementation, and proficiency in PyTorch or JAX are essential. This role offers competitive compensation and meaningful equity in a small, high-caliber team.

Qualifications

• Strong background in machine learning fundamentals and deep learning.
• Hands‑on experience implementing model architectures from scratch.
• Comfortable working in PyTorch or JAX.

Responsibilities

• Research and develop new neural network architectures.
• Design and run architecture-level experiments.
• Collaborate with inference and systems engineers.

Skills

Machine learning fundamentals

Deep learning

Model architectures implementation

Attention mechanisms

Experience in PyTorch or JAX

Training dynamics

Tools

PyTorch

JAX

Job description

About the Role

We’re looking for a Machine Learning Engineer focused on AI architecture research to help design, prototype, and validate next-generation model architectures. You’ll work at the intersection of research and production — turning new ideas into scalable, real-world systems.

This role is ideal for someone who enjoys questioning architectural assumptions, experimenting with novel model designs, and pushing beyond standard Transformer-style approaches.

What You’ll Work On

• Research and develop new neural network architectures (e.g. alternatives or extensions to Transformers, recurrent / hybrid models, long-context systems)

• Design and run architecture-level experiments (scaling laws, memory mechanisms, compute trade-offs)

• Prototype models end-to-end — from research code to training-ready implementations

• Collaborate with inference and systems engineers to ensure architectures are deployable and efficient

• Analyze model behavior, failure modes, and inductive biases

• Read, reproduce, and extend cutting-edge research papers

• Contribute to internal research notes, benchmarks, and open-source efforts (where applicable)

What We’re Looking For

• Strong background in machine learning fundamentals and deep learning

• Hands‑on experience implementing model architectures from scratch

• Solid understanding of:

  • Attention mechanisms, RNNs, state‑space models, or hybrid architectures

  • Training dynamics, scaling behavior, and optimization

  • Memory, latency, and compute constraints at the model level

• Comfortable working in PyTorch or JAX

• Ability to move fluidly between theory, experimentation, and engineering

• Clear communicator who can explain architectural trade‑offs

Nice to Have

• Experience with non‑Transformer architectures (RNN variants, SSMs, long‑context models)

• Background in research‑driven startups or open‑source ML projects

• Experience with large‑scale training or custom training loops

• Publications, preprints, or notable research contributions

• Familiarity with inference optimization and deployment constraints

Why Join

• Work on core model architecture, not just fine‑tuning

• Direct influence on the technical direction of a Series‑A company

• Small, high‑caliber team with fast feedback loops

• Opportunity to ship research into production

• Competitive compensation + meaningful equity