Efficiency Research Engineer
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Requirements
• Deep expertise in at least one area: model efficiency, distributed systems, hardware acceleration, or algorithmic optimization • Systems thinking ability to understand and optimize across the full ML stack • Strong programming skills in Python. Experience with deep learning frameworks (PyTorch, JAX, TensorFlow) • Knowledge of model optimization techniques (RLHF, finetuning) • Experience in an industry lab with computing at scale and/or fast-paced startup environment • Nice to have: PhD in computer science, and experience with algorithm design • Above all, we're looking for great teammates who make work feel lighter and aren't afraid to go out on a limb with bold ideas. You don't need to be perfect, but you do need to be adaptable. We encourage you to apply, even if you don't check every box.
Responsibilities
• Innovation: lead our focused bets on real time adaptation, which include innovating on algorithmic recipes which result in large real time gains. • Cross-Stack Optimization: collaborate across software, hardware, and algorithmic domains to achieve system-wide efficiency gains. • Research & Development: explore new research directions in efficient machine learning, alignment, inference time scaling and adaptable systems. We will have a focus on gradient free techniques which produce large performance gains, as well as data efficient techniques which allow for rapid alignment and adaptation.
Benefits
• Flexible work: In-person collaboration in the Bay Area, a distributed global-first team, and quarterly offsites. • Adaption Passport: Annual travel stipend to explore a country you've never visited. We're building intelligence that evolves alongside you, so we encourage you to keep expanding your horizons. • Lunch Stipend: Weekly meal allowance for take-out or grocery delivery. • Well-Being: Comprehensive medical benefits and generous paid time off.