Translate AI research into production systems for scientific R&D: build evals/benchmarks, design data ingestion and representation for messy multimodal scientific data, implement LLM-based and other agentic systems, and provide technical leadership to define product and engineering direction.
We’re building the scientific intelligence platform for the physical world, to accelerate breakthroughs in semiconductors, batteries, advanced materials, aerospace, and beyond. These industries represent trillions in spend, but are still trapped in outdated software. We’re changing that by building the intelligence layer to help scientists and engineers move faster and solve core problems -- R&D through manufacturing.
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:
- Translate cutting-edge AI research into production-grade systems.
- Build evals and benchmarks to rigorously measure model and agent performance across a wide variety of scientific tasks.
- 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, helping to define Altara’s technical vision and engineering culture as we scale.
We would love to meet you if you:
- Care about solving meaningful scientific challenges and disrupting the industry.
- Have experience at the intersection of research and engineering: comfortable reading papers one hour and debugging data pipelines the next.
- Have applied AI/ML engineering and research experience, especially with architecting multi-agent workflows, working with multimodal data, or building search and knowledge retrieval systems.
- Consider yourself product-minded and customer-first.
- 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.
Bonus points if you have:
- Shipped 0-to-1 products at a startup, applied AI lab, or other high-growth environments before.
- Experience or deep curiosity in science.
Learn More about Altara
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- Follow us on LinkedIn and X
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