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RESEARCHER, EFFICIENT INFERENCE
MakerMaker
San Francisco, USA
Full-time
Senior · 5+ years experience
Onsite
Discovered 1 weeks ago
PyTorchJAX
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PyTorchJAX
Role Overview
You'll be researching making models efficient: quantization, speculative decoding, sparse and structured attention, distillation, mixture-of-experts inference, and the training-time techniques that make those methods possible. The work spans algorithm design, careful evaluation, and pushing methods to where they actually run.
This is a senior research role with a clear engineering edge. You'll spend time at the intersection of model architecture and inference performance, designing methods that move accuracy/latency/cost trade-offs in our favor (then partnering with engineers to make those wins real in production).
Key Skills for This Role
PyTorchJAX
Full Job Posting
ABOUT THE COMPANY
We're building autonomous research agents for recursive self-improvement (multi-agent systems that propose, run, and analyze machine learning experiments). We're a small team based in San Francisco, on-site
ABOUT THE ROLE
You'll be researching making models efficient: quantization, speculative decoding, sparse and structured attention, distillation, mixture-of-experts inference, and the training-time techniques that make those methods possible. The work spans algorithm design, careful evaluation, and pushing methods to where they actually run.
This is a senior research role with a clear engineering edge. You'll spend time at the intersection of model architecture and inference performance, designing methods that move accuracy/latency/cost trade-offs in our favor (then partnering with engineers to make those wins real in production).
WHAT YOU'LL DO
- Research and develop quantization methods: post-training quantization, quantization-aware training, mixed-precision regimes, low-bit-width arithmetic
- Design and evaluate speculative decoding approaches: draft models, tree attention, parallel speculation, lookahead decoding
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