Researcher, Efficient Inference

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

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).

• 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

• 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

• 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

• 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)