Specialism: AI/ML

Project: This company specializes in building the AI economy for humans by focusing on augmentation rather than automation. Their vision is for every individual to have a personal AI aligned with their goals and values, ensuring people remain empowered and relevant in a post-AGI world. As a public benefit corporation, they are committed to making AI a tool that enhances human potential rather than replacing it. Backed by leading investors, AI safety funds, and advisors from top AI organizations, the company is guided by a mission-driven team with backgrounds in machine learning research, product leadership, political strategy, and world-class academic training.

Key Skills: vLLM, S-LoRA, Punica, LoRAX, TEEs, Privacy-first Infrastructure, Nvidia Confidential Computing, Intel TDX, AMD SEV-SNP

Location: Remote

Role Detail: We are searching for a Founding Machine Learning Infrastructure Engineer who will build and scale the systems that enable the creation and deployment of thousands and eventually millions of personalized fine-tuned models for customers. In this role, you will monitor and optimize real-world model serving to achieve low latency and cost efficiency while integrating privacy-preserving technologies that ensure user data and models remain fully secure and accessible only to the individual user. The ideal candidate brings deep expertise across the machine learning stack with the ability to reason from high-level transformer architectures down to GPU performance at the hardware level. Success in this position requires the ability to execute quickly, thrive in a fast-moving startup environment, and a mission-driven commitment to building AI systems that empower people rather than replace them.

Requirements:

• Experience working in a fast-paced AI startup or leading AI research lab.
• Proven experience deploying machine learning systems at scale, with familiarity in frameworks such as vLLM, S-LoRA, Punica, or LoRAX.
• Strong background in privacy-first infrastructure, with knowledge of confidential computing principles and the ability to assess both technical and real-world confidentiality and security risks.
• Hands-on experience with secure enclaves, trusted execution environments (TEEs), code measurement, and remote attestation.
• Familiarity with confidential computing technologies such as Nvidia Confidential Computing, Intel TDX, or AMD SEV-SNP.
• Openness to nontraditional backgrounds — candidates with unconventional experience are encouraged to apply.