✓ Screened eligible
Why you can actually get this job
The posting explicitly states 'Remote (world)' with no geographic, work authorization, timezone, language, or residency restrictions. It is a full-time employee role with no visa sponsorship language that would trigger G1. The target user meets all technical requirements and faces no hard gates.
“Remote (world)”
Geographic scope — Explicit worldwide remote scope with no geographic restrictions stated.
“Remote-friendly, async-first environment”
Work authorization — No work authorization requirement, visa sponsorship restriction, or citizenship mandate stated anywhere in the posting.
“async-first environment”
Timezone — Async-first signals no mandatory timezone overlap requirement.
About the Role
We’re looking for a Machine Learning Engineer focused on model distillation to help us build smaller, faster, and more efficient models without sacrificing quality. You’ll work at the intersection of research and production—taking cutting-edge techniques and turning them into systems that scale.
- This is a hands-on role with real ownership: you’ll design distillation pipelines, run large-scale experiments, and ship models used in production.
What You’ll Do
- Design and implement knowledge distillation pipelines (teacher–student, self-distillation, multi-teacher, etc.)
- Distill large foundation models into smaller, faster, and cheaper models for inference
- Run and analyze large-scale training experiments to evaluate quality, latency, and cost tradeoffs
- Collaborate with research to translate new distillation ideas into production-ready code
- Optimize training and inference performance (memory, throughput, latency)
- Contribute to internal tooling, evaluation frameworks, and experiment tracking
- (Optional) Contribute back to open-source models, tooling, or research
What We’re Looking For
- Strong background in machine learning or deep learning
- Hands-on experience with model distillation (LLMs or other neural networks)
- Solid understanding of training dynamics, loss functions, and optimization
- Experience with PyTorch (or JAX) and modern ML tooling
- Comfort running experiments on multi-GPU or distributed setups
- Ability to reason about model quality vs. performance tradeoffs
- Pragmatic mindset: you care about shipping, not just papers
Nice to Have
- Experience distilling LLMs or large sequence models
- Experience with inference optimization (quantization, pruning, kernels, etc.)
- Familiarity with evaluation for language models
- Open-source contributions or research publications
- Experience in early-stage or fast-moving startups
Why Join
- Work on core model quality and cost efficiency —not side projects
- High ownership and direct impact on product and roadmap
- Small, senior team with strong research + engineering culture
- Competitive compensation + meaningful equity
Remote-friendly, async-first environment
Originally posted on Himalayas
