Applied AI Engineer (In-Person) at Idelsoft

Job description

We're recruiting on behalf of a stealth, seed-stage AI company building agent systems for a highly regulated, physical-world engineering domain. Their customers are large global enterprises with real safety constraints, real compliance requirements, and decades of accumulated technical data that no one has been able to make an AI reason over reliably. That's the problem you'd be hired to solve. The company is backed by two of the most recognized firms in venture, plus a strategic investor from the industry they serve. The founding team is unusual: AI researchers from a top university lab, a global legal and compliance leader from a major technology company, senior operating executives from large industrial manufacturers, and a former head of a US federal regulatory agency. It's a genuine mix of AI, deep engineering, and policy — which is exactly what the problem requires. You'd be among the first technical hires, owning mission-critical systems end to end. What You'll Own • Agent workflows and infrastructure. Core systems for domain-specific engineering agents — intelligent context management, novel indexing approaches, and advanced data structures. • Reasoning over complex design data. Agents and models that interpret, reason over, and manipulate highly structured technical data, integrated with enterprise-grade engineering tools. • Post-training. Customizing and fine-tuning models for a domain where generic performance isn't good enough. • Direct customer collaboration. Working alongside customers to deliver workflows that are reliable, intuitive, and trusted in production — not demos. • Speed. Prototype, validate with real users, ship frequently. • Company shape. Influence on engineering culture, architecture decisions, and product direction. What We're Looking For Must-have • Bachelor's or higher in Computer Science, Machine Learning, or equivalent practical experience • Strong familiarity with LLM architectures and current agent research • Excellent programming skills with ML libraries — PyTorch, Hugging Face, W&B • Experience building production ML or AI systems, not just research prototypes • Solid practical experience with search and information retrieval • Moves fast from idea to production, with genuine care for customer experience • Based in or willing to relocate to San Francisco (or exceptional enough to justify remote) Nice-to-have • 2+ years in applied ML, backend engineering, or AI infrastructure • Experience with machine learning over geometric or highly structured technical data • Post-training or fine-tuning experience • Background in a domain with real physical or regulatory constraints • Prior founding experience • High-impact contributions or published work at a top-tier AI lab or team Culture The team is intentional with its time — hard work on meaningful problems, and protected personal time. Success is defined by customer outcomes and by the team's own professional growth. Expect ambiguity, real constraints, and high expectations. Benefits • Unlimited PTO • Full health insurance • Free lunch and dinner • Company equipment provided

Requirements

Must-haves

You'd be among the first technical hires, owning mission-critical systems end to end.
What You'll Own
Agent workflows and infrastructure. Core systems for domain-specific engineering agents — intelligent context management, novel indexing approaches, and advanced data structures.
Reasoning over complex design data. Agents and models that interpret, reason over, and manipulate highly structured technical data, integrated with enterprise-grade engineering tools.
Post-training. Customizing and fine-tuning models for a domain where generic performance isn't good enough.
Direct customer collaboration. Working alongside customers to deliver workflows that are reliable, intuitive, and trusted in production — not demos.
Speed. Prototype, validate with real users, ship frequently.
Company shape. Influence on engineering culture, architecture decisions, and product direction.
Bachelor's or higher in Computer Science, Machine Learning, or equivalent practical experience
Strong familiarity with LLM architectures and current agent research
Excellent programming skills with ML libraries — PyTorch, Hugging Face, W&B
Experience building production ML or AI systems, not just research prototypes
Solid practical experience with search and information retrieval
Moves fast from idea to production, with genuine care for customer experience
Based in or willing to relocate to San Francisco (or exceptional enough to justify remote)
2+ years in applied ML, backend engineering, or AI infrastructure

Nice-to-haves

Experience with machine learning over geometric or highly structured technical data
Post-training or fine-tuning experience
Background in a domain with real physical or regulatory constraints
Prior founding experience
High-impact contributions or published work at a top-tier AI lab or team

Application prep

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Keywords to mirror

aimachine learningllmml

Platform: manatal
Effort: medium

Benefits

Benefits detected in the listing.

Health Insurance

Pto