Adept
AI research and product lab building models for enterprise workflow automation.
Typical Backgrounds
Types of experience professionals commonly develop
Roles Companies Often Recruit
Common roles when hiring Adept-caliber talent
- AI Engineers
- Research Engineers
- Applied Scientists
- Infrastructure Engineers
- Founding Engineers
- Engineering Leaders
Related Resources
Salary guides, hiring guides, market reports and case studies
Talent market position
Adept built its early reputation around AI agents capable of taking action inside software interfaces, positioning its talent pool at the intersection of applied ML, agentic systems research, and UI/browser automation engineering. Its team included researchers with backgrounds from Google Brain and DeepMind, and that lineage carries strong signal among candidates and hiring managers focused on foundation model and agent research.
The company's trajectory shifted meaningfully after key leadership and technical talent moved to other large AI labs in 2024, and this event is one of the more publicly documented executive/team moves in the AI talent market. Recruiters evaluating Adept alumni should account for this: a meaningful share of the company's most senior technical talent has already dispersed to other frontier labs and startups, which shapes what remains in the market versus what has already been absorbed elsewhere.
Remaining and departed alumni are both relevant to a search: those who moved to other labs bring frontier-adjacent agent research experience, while earlier-tenure engineers who stayed or moved to other startups bring practical agentic product-building experience.
Org structure and titles
As a smaller, research-oriented startup, Adept ran a compact structure: a core ML research team focused on agentic model capability, and an applied engineering team building the browser/interface automation product layer. Titles followed a standard Research Scientist/Engineer and Software Engineer structure without extensive leveling.
Given the company's size and research focus, most engineers carried broad scope across both model capability and product integration, which is a useful signal when evaluating what an individual candidate actually owned.
Hiring bar and process, as commonly reported
Candidates commonly described a research-oriented process: a technical screen on ML fundamentals and agent/RL-adjacent topics, followed by a compact onsite loop emphasizing research judgment and the ability to reason about tool-use and multi-step agent behavior.
The bar is commonly described as high specifically on agentic reasoning and interface-automation problems, a narrower and more specialized skill set than general LLM engineering, reflecting the company's specific technical focus.
Compensation posture
| Level / Function | Typical total comp range | Equity form | Notes |
|---|---|---|---|
| Research scientist/engineer (agent research) | $300K–$550K | Equity (pre-IPO, historical) | Comp reflects the company's earlier funding rounds; individual outcomes vary given the 2024 team transition. |
| Applied/product engineering | $220K–$380K | Equity (pre-IPO, historical) | Smaller function relative to research; comp closer to general AI startup norms. |
Directional ranges reflect the company's historical hiring; candidates from this background should be evaluated on current market comp for their target role, not on legacy Adept equity value.
How Adept alumni transfer
| Origin team | Strengths they bring | Best-fit destination | Watch-outs |
|---|---|---|---|
| Agentic model research | Deep, relatively rare expertise in tool-use and multi-step agent reasoning | AI labs and startups building autonomous agent products | Small, specialized talent pool; high demand means fast competing offers. |
| Browser/interface automation engineering | Practical experience building AI systems that operate real software interfaces | Agentic product startups (automation, RPA-adjacent AI) | Fewer reps with large-scale distributed training infrastructure. |
How to recruit out of Adept
Given the company's well-documented team transition, candidates with an Adept background are commonly already fielding inbound interest from other agent-focused labs and startups; searches should move quickly and expect a compressed decision timeline once a candidate is in an active process.
Motivation for a move commonly centers on wanting to rejoin a team with clearer resourcing and a stable technical roadmap, given the disruption the company experienced. A pitch emphasizing organizational stability and a credible agent-research roadmap performs well with this specific candidate pool.
Frequently asked questions
Is Adept still an active source of AI talent to recruit from?
A meaningful share of its most senior technical staff already moved to other frontier labs in a well-documented 2024 transition. Remaining or subsequently-departed alumni still carry valuable specialized agent-research experience worth sourcing.
What is distinctive about an Adept engineering background?
Deep, relatively rare expertise in agentic reasoning and interface automation, specifically building AI systems that can take multi-step actions inside real software. This is narrower and scarcer than general LLM engineering experience.
How fast do Adept-background candidates move through a process?
Often quickly, since many are already fielding inbound interest from other agent-focused labs and startups given the public visibility of the company's 2024 transition. Searches should expect compressed decision timelines.
What should I know about Adept equity when evaluating a candidate?
Treat legacy equity value as historical context, not a benchmark; evaluate any new offer against current market comp for the target role rather than trying to match a prior company's cap table outcome.
What motivates an Adept alum to consider a new role?
Commonly, a desire for organizational stability and a clear, well-resourced technical roadmap, given the disruption the company experienced. Pitches emphasizing stability and roadmap clarity resonate strongly.