# Machine Learning Engineer — Training Optimization at Featherlessai

> Remote · region unclear · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Afeatherlessai%3Adcbb7273-4d0e-4fd0-bb33-1e0ad99eca69 (read 2026-11-12).

## Facts (observed)

- Company: [Featherlessai](https://alderhq.app/companies/featherlessai)
- Location text: Remote (world)
- Where it can be done from (inferred): Remote · region unclear (evidence: "Location: Remote (world)")
- Posted: 2026-01-22 (295 days ago)
- First seen by alder: 2026-11-04 · last verified: 2026-11-08
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/featherlessai/dcbb7273-4d0e-4fd0-bb33-1e0ad99eca69
- Skills listed (inferred): Node.js, PyTorch, LLMs, Distributed systems

## Pay and sponsorship

- Posted pay: not posted

## Description (the employer's text)

### 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

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Afeatherlessai%3Adcbb7273-4d0e-4fd0-bb33-1e0ad99eca69 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.