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)