About Us:
Hippocratic AI is building safety-focused large language model (LLM) for the healthcare industry. Our team comprised of ex-researchers from Microsoft, Meta, Nvidia, Apple, Stanford, John Hopkins and HuggingFace are reinventing the next generation of foundation model training and alignment to create AI-powered conversational agents for real time patient-AI interactions.
About the Role
We're seeking an experienced LLM Inference Engineer to optimize our large language model (LLM) serving infrastructure. The ideal candidate has:
Extensive hands-on experience with state-of-the-art inference optimization techniques
A track record of deploying efficient, scalable LLM systems in production environments
Key Responsibilities
Design and implement multi-node serving architectures for distributed LLM inference
Optimize multi-LoRA serving systems
Apply advanced quantization techniques (FP4/FP6) to reduce model footprint while preserving quality
Implement speculative decoding and other latency optimization strategies
Develop disaggregated serving solutions with optimized caching strategies for prefill and decoding phases
Continuously benchmark and improve system performance across various deployment scenarios and GPU types
Required Qualifications
2+ years of experience optimizing LLM inference systems at scale
Proven expertise with distributed serving architectures for large language models
Hands-on experience implementing quantization techniques for transformer models
Strong understanding of modern inference optimization methods, including:
Proficiency in Python and C++
Experience with CUDA programming and GPU optimization (familiarity required, expert-level not necessary)
Preferred Qualifications
Contributions to open-source inference frameworks such as vLLM, SGLang, or TensorRT-LLM
Experience with custom CUDA kernels
Track record of deploying inference systems in production environments
Deep understanding of performance optimization systems
Why Join Our Team
Innovative Mission: We are developing a safe, healthcare-focused large language model (LLM) designed to revolutionize health outcomes on a global scale.
Visionary Leadership: Hippocratic AI was co-founded by CEO Munjal Shah, alongside a group of physicians, hospital administrators, healthcare professionals, and artificial intelligence researchers from leading institutions, including El Camino Health, Johns Hopkins, Stanford, Microsoft, Google, and NVIDIA.
Strategic Investors: We have raised a total of $278 million in funding, backed by top investors such as Andreessen Horowitz, General Catalyst, Kleiner Perkins, NVIDIA’s NVentures, Premji Invest, SV Angel, and six health systems.
World-Class Team: Our team is composed of leading experts in healthcare and artificial intelligence, ensuring our technology is safe, effective, and capable of delivering meaningful improvements to healthcare delivery and outcomes.
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