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.

Location: Remote

Role Detail: We are searching for a Machine Learning Engineer who will specialize in building systems that adapt AI models to think, reason, and reflect the unique judgment, style, and values of individual users. This role involves designing and implementing parameter-efficient fine-tuning techniques, creating and testing synthetic and real-world data pipelines, and evaluating model quality based on nuanced human relevance rather than public benchmarks. You will play a critical role in developing systems that can securely and privately fine-tune models for anyone, while also building guardrails to minimize misuse and reduce catastrophic risks. Success in this position requires deep expertise in transformers, a strong intuition for how datasets shape model behavior, and the ability to execute rapidly in an early-stage startup environment. The ideal candidate is mission-driven, eager to translate ML theory into practical applications, and comfortable wearing many hats to help advance the company’s vision of ensuring AI serves people, not the other way around.

Requirements:

• Hands-on experience running fine-tuning or post-training experiments with large language models (LLMs).
• Background working in a fast-paced AI startup or within a leading AI research lab.
• Demonstrated track record of publishing machine learning research, whether through peer-reviewed papers or in-depth technical writing such as blog posts.
• Strong academic foundation in machine learning research, which may include a PhD, research-focused master’s degree, or participation in programs like MATS or the Anthropic Fellows Program.
• Openness to unconventional paths — candidates with diverse or nontraditional backgrounds are encouraged to apply.