RESEARCHER, EFFICIENT INFERENCE

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 decisionsWHAT WE'RE LOOKING FORStrong 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 itTrack 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 venuesNICE TO HAVEPhD 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 computationTHIS ROLE IS PROBABLY NOT FOR YOU IFYou 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)