Machine Learning Engineer — Distillation
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 a Machine Learning Engineer focused on model distillation to help us build smaller, faster, and more efficient models without sacrificing quality.
This is a hands-on role with real ownership: you’ll design distillation pipelines, run large-scale experiments, and ship models used in production.
Design and implement knowledge distillation pipelines (teacher-student, self-distillation, multi-teacher)
Distill large foundation models into smaller, faster, cheaper inference models
Run and analyze large-scale experiments on quality, latency, and cost tradeoffs
Turn research distillation ideas into production-ready code with the research team
Optimize training and inference performance (memory, throughput, latency)
From the employer’s posting
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.
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)
What you’ll bring
All qualificationsCore experience
- Hands-on experience with model distillation (LLMs or other neural networks)
- Strong background in machine learning or deep learning
- Experience with PyTorch (or JAX) and modern ML tooling
- Solid understanding of training dynamics, loss functions, and optimization
- Comfort running experiments on multi-GPU or distributed setups
Qualification wording
Hands-on experience with model distillation (LLMs or other neural networks)
Strong background in machine learning or deep learning
Experience with PyTorch (or JAX) and modern ML tooling
Solid understanding of training dynamics, loss functions, and optimization
Comfort running experiments on multi-GPU or distributed setups
Tools in this posting
- PyTorch
Source — Tool mentions in context
- 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
Posting record
Report an errorOpen 4 monthsWe first saw this listing 144 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 · 144 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 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
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)
- Competitive compensation + meaningful equity - Remote-friendly, async-first environment
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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