Conducts ML research on LLMs and audio models to enhance real-time voice agents' reasoning, latency, and conversational quality. Prototypes models, designs evaluations, and bridges research to production systems requiring strong PyTorch expertise and experimental mindset.
Cash: $225,000 - $400,000 base salary
Equity: Offers Equity
Other Benefits: 100% coverage for medical, dental, and vision insurance; $70/day DoorDash credit; $200/month wellness reimbursement; $300/month commuter reimbursement; $75/month phone bill reimbursement; $50/month internet reimbursement
Leads development of performance optimizations for ML models across graph, kernel, and system levels, advances Thunder compiler with new passes and tools, and ensures seamless PyTorch Lightning integration. Requires strong PyTorch expertise, optimization techniques, and distributed systems knowledge.
Owns end-to-end ML research initiatives developing novel architectures, training methods, and evaluation for clinical intelligence using longitudinal patient data. Requires strong ML foundation, PyTorch experience, and ability to drive ambiguous high-stakes problems to validated results.
Owns end-to-end production ML systems for clinical workflows, including training/fine-tuning LLMs for medical reasoning and question answering. Requires strong ML/software engineering, PyTorch experience, and ability to handle high-stakes ambiguity with real patient impact.
Research Engineer advancing video/image generation models for AI characters, leading fine-tuning, novel architectures, data pipelines, and optimizations using PyTorch and multimodal techniques. Requires expertise in generation models and distributed training.
Develops alignment algorithms, data pipelines, and sampling methods to optimize post-training AI models for performance and efficiency. Requires PhD or equivalent, ML expertise including reinforcement learning and transformers, and production code experience.