Machine Learning Engineer — Training Optimization
Remote (world)
- Pay
- Pay not listed
- Work setting
Remote stated — work setting source
Listed location: Remote (world)
Read the full posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Research — team source
Department: Research
From the employer’s posting
What you’ll work on
Full postingWe’re looking for an ML Engineer focused on training optimization to help us scale and improve large-scale model training.
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.
Optimize large-scale training pipelines for throughput, convergence, stability and cost
Improve distributed training strategies across data, model and pipeline parallelism
Tune optimizers, schedulers, batch sizes and precision (bf16, fp16, fp8)
Profile bottlenecks and make systems-level changes to cut training time and cost
Build and maintain training infrastructure with checkpointing and fault tolerance
From the employer’s posting
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.
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
Build and maintain robust training infrastructure (checkpointing, fault tolerance, reproducibility)
What you’ll bring
All qualificationsCore experience
- Strong experience training large neural networks (LLMs or similarly large models)
- Hands-on training optimization experience, not just model usage
- Solid grasp of backpropagation, optimization algorithms and training dynamics
- Experience with PyTorch
- Comfort working close to hardware (GPUs, memory, networking constraints)
Qualification wording
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
Experience with PyTorch (required)
Comfort working close to hardware (GPUs, memory, networking constraints)
Tools in this posting
- PyTorch
Source — Tool mentions in context
- Distributed systems for ML training - Experience with PyTorch (required) - Comfort working close to hardware (GPUs, memory, networking constraints)
Posting record
Report an errorOpen 4 monthsWe first saw this listing 143 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jun 2, 2026 · 143 days ago
- Recorded sightings
- 82
- Last seen by us
- Oct 23, 2026
- Employer says posted
- Jan 22, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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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
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Remote (world)
- Contributions to open-source ML infrastructure or research codebases - Exposure to non-Transformer architectures (RNNs, hybrid models, etc.) Why Join Us
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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