Featherless AI
More roles at Featherless AIMachine Learning Engineer — Training Optimization
Remote work allowed only within certain countries or regions.
Employer listed it 9 months ago · Added 5 weeks ago
Been open since 9 months ago. Long-running listings are sometimes left up after the role is filled.
Salary
Not stated
Location
Timezone
Not stated
Contract
Full-time
Experience
Mid
Category
Data
The employer did not publish a salary, and we have too few comparable roles to estimate one.
What the advert says
Quoted word for word from the employer's advert. Where it says nothing, we say so.
- Working hours
- Not mentioned in the advert
- Where you can work from
Exposure to non-Transformer architectures (RNNs, hybrid models, etc.)
- Contract
- Not mentioned in the advert
- Experience asked for
- Not mentioned in the advert
- Pay
- Not mentioned in the advert
Remote flexibility
Region Restricted
Remote work is allowed, but the listing limits candidates to Remote (world).
What the employer says
- Source listing states candidate location: "Remote (world)"
What Nomaders makes of it
- Nomaders could not match this to a wider region
- Check the original posting
The quotes above are the employer's own words; the reading is ours. Always check the original listing and employment terms before working from another country.
Before you apply
Experience: employer states
Written as a requirement
- “Experience with PyTorch (required)”
Questions worth asking: Nomaders interpretation
- No pay is stated. Ask for the salary range before investing time in interviews.
- The advert does not say whether this is employment or a contractor arrangement. Ask, as it affects tax and benefits.
Produced automatically from this advert's text; not reviewed by a person. The employer's own page is the final word.
About the role
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
Requirements
The employer hasn't listed requirements separately, they're described in the role summary above and on the original listing.
Benefits
No benefits package published with this listing. Ask about it at first interview.
How to apply
- 1Check the flexibility label above, region restricted, matches where you plan to live and work.
- 2Tailor your CV to the role at Featherless AI, mentioning your remote working experience.
- 3Apply directly on the employer's careers page using the button below. Nomaders never handles your application.
Found 1mo ago. Last checked today. Always confirm the details on the original posting, salary and location can change after publication.
Listing sourced from Company boards.
Pay not stated · You'll be taken to the employer's careers page.