Telecommut
We have a once-in-a-generation opportunity to create a category-defining company at the intersection of AI and the physical sciences. And we’re building a world-class team to do it.
We recently raised a seed round led by Greylock, with participation from Neo, BoxGroup, Liquid 2, and top angels including Jeff Dean + leadership at OpenAI and AMD. Our team (ex-Applied Intuition, SpaceX, Warp, Jane Street, Microsoft) works in-person in San Francisco. Our office is located walking distance from 4th and King Caltrain station.
What You’ll Do
- Architect and build the early foundations of Altara’s core product and infrastructure.
- Ship end-to-end features across the stack, balancing speed, scope, and user experience.
- Develop data ingestion and representation systems that can automatically extract structure and insight from multimodal, messy real-world scientific data (e.g., R&D databases, spreadsheets, images, technical documents, etc).
- Build both LLM-based agentic systems and implementations beyond LLMs.
- Act as a technical leader across the organization, mentoring top talent and helping define Altara’s technical vision and engineering culture as we scale.
- Care about solving meaningful scientific challenges and disrupting the industry, not just building another SaaS tool.
- Have shipped 0-to-1 products at a startup, applied AI lab, or other high-growth environments before.
- Consider yourself a product-first engineer - you start from the user problem, not the technology, and make smart technical tradeoffs to deliver impact fast.
- Love to learn, bias toward action, and enjoy taking full ownership of problems end-to-end.
- Thrive in fast-paced, unstructured environments with evolving requirements.
- Already use AI tools daily and think critically about how to push their limits.
- Worked at an early stage startup, founded a company, or plan to start one someday.
- Applied AI/ML engineering and research experience, especially with building search and ranking tools, LLM-based applications, agentic workflows, and computer vision.
- Experience or deep curiosity in science.
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