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, well-funded team that works onsite in San Francisco.

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

Investigate training-time efficiency methods that compose well with inference: distillation, sparse attention, mixture-of-experts, low-rank adaptation, pruning

Run controlled experiments at production scale; characterize what works on real workloads, not just toy benchmarks

Co-design methods with the inference engineering team: push results to where they actually run, not stop at the paper

Read deeply across the efficient ML / efficient inference literature; translate the most useful ideas into our stack

Publish when the work warrants it; share findings internally

Partner with model and training researchers so efficiency choices align with model architecture and post-training decisions

WHAT WE'RE LOOKING FOR

Strong track record of ML research on efficiency methods: quantization, speculative decoding, distillation, MoE, sparse attention, or adjacent

5+ years of hands-on research experience

Deep familiarity with both training and inference performance characteristics

Fluent in PyTorch, Jax or equivalent; comfortable working at the kernel and serving-framework level when methods require it

Track record of moving efficiency research from prototype to production

Strong statistical expertise: you'd notice a flawed comparison before someone else points it out

Strong written communication

Published research at NeurIPS, ICML, ICLR, MLSys, or comparable venues

NICE TO HAVE

PhD in ML, systems, or related field

Open-source contributions to quantization, speculative-decoding, or efficient-inference libraries

Experience with hardware-aware optimization and accelerator-specific tooling

Background in numerical methods, low-precision arithmetic, or

approximate computation

THIS ROLE IS PROBABLY NOT FOR YOU IF

You want to focus on pretraining large models from scratch (that's a different role)

You prefer abstract algorithmic research without hands-on implementation

You want a fixed benchmark with stable targets (our targets shift with what our models actually need to do)