Machine Learning Engineer — Training Optimization

Featherless AI; Poland; On-site; PLN 180,000 - 260,000; Full time

Benefits offered by this job

Competitive compensation

Meaningful equity

Job summary

A technology start-up in Poland is seeking an experienced ML Engineer focused on training optimization to enhance large-scale model training. In this high-impact role, you will optimize training pipelines for speed and cost while closely collaborating with researchers. The ideal candidate has strong experience in training large neural networks and is proficient in PyTorch. The role offers competitive compensation and equity in a fast-paced startup environment.

Qualifications

• Strong experience training large neural networks (LLMs or similarly large models).
• Hands-on experience with training optimization (not just model usage).
• Solid understanding of backpropagation, optimization algorithms, and training dynamics.

Responsibilities

• Optimize large-scale model training pipelines.
• Improve distributed training strategies.
• Tune optimizers, schedulers, and precision.
• Reduce training time and compute cost.
• Collaborate with researchers on architecture-aware training strategies.
• Build and maintain training infrastructure (checkpointing, fault tolerance).
• Evaluate new training techniques and push performance metrics.

Skills

Training optimization

Backpropagation

Optimization algorithms

Distributed systems for ML training

PyTorch

Job description

About the Role

We’re looking for an ML Engineer focused on training optimization to help us scale and improve large-scale model training. You’ll work at the intersection of research and production, optimizing training pipelines for speed, stability, and cost—while collaborating closely with researchers pushing model architecture and capability forward.

This is a high-impact role with real ownership: your work directly affects how fast we can iterate, how large we can scale, and how efficiently we deploy new models.

What You’ll Do

• Optimize large-scale model training pipelines (throughput, convergence, stability, and cost)

• Improve distributed training strategies (data, model, and pipeline parallelism)

• Tune optimizers, schedulers, batch sizing, and precision (bf16 / fp16 / fp8)

• Reduce training time and compute cost via profiling, bottleneck analysis, and systems-level improvements

• Collaborate with researchers on architecture-aware training strategies

• Build and maintain robust training infrastructure (checkpointing, fault tolerance, reproducibility)

• Evaluate and integrate new training techniques (e.g. gradient checkpointing, ZeRO, FSDP, custom kernels)

• Own training performance metrics and continuously push them forward

What We’re Looking For

• Strong experience training large neural networks (LLMs or similarly large models)

• Hands‑on experience with training optimization (not just model usage)

• Solid understanding of:

  • Backpropagation, optimization algorithms, and training dynamics

  • Distributed systems for ML training

• Experience with PyTorch (required)

• Comfort working close to hardware (GPUs, memory, networking constraints)

• Ability to move fluidly between research ideas and production‑ready code

Nice to Have

• Experience with large‑scale distributed training (multi‑node, multi‑GPU)

• Familiarity with DeepSpeed, FSDP, Megatron, or custom training stacks

• Experience optimizing training on AMD or NVIDIA GPUs

• Contributions to open‑source ML infrastructure or research codebases

• Exposure to non‑Transformer architectures (RNNs, hybrid models, etc.)

Why Join Us

• Real ownership at Series‑A stage — your work shapes the company’s trajectory

• Work on cutting‑edge models and training systems at scale

• Small, highly technical team with fast feedback loops

• Strong emphasis on engineering quality and research rigor

• Competitive compensation + meaningful equity