Autostep (YC P26) Is Hiring AI/Fullstack Engineers and a Chief of Staff
Career Opportunities at Autostep (YC P26)

Autostep is positioning itself as the "P&L for knowledge work," creating a specialized desktop application designed to identify and quantify repetitive operational friction within a company.
🚀 The Mission
Autostep analyzes organizational behavior to uncover hidden inefficiencies. By monitoring workflows, the app identifies where a company is bleeding money losing capital through invisible bottlenecks.
Once these leaks are identified, Autostep provides a roadmap for high-leverage remediation via:
- Automated AI Agents (custom-built)
- Strategic Process Overhauls
- Optimization of current software stacks
- Integration of superior third-party vendors
The Logic Flow
💼 Open Positions
Autostep is currently seeking founding team members to join them in San Francisco, CA. New graduates are encouraged to apply.
| Role | Primary Focus | Location | Experience Level |
|---|---|---|---|
| Founding Chief of Staff | GTM + Operations | San Francisco | Any / New Grads |
| Founding Engineer | ML + Forward Deployment | San Francisco | Any / New Grads |
How to apply: Visit the official portal at
https://autostep.ai/
💰 Backing & Pedigree
The company is supported by Y Combinator, Neo, and a roster of elite founders and executives:
- Walden Yan: Co-Founder of Cognition (\approx \26\text{B}$)
- Erik Goldman: Co-Founder of Vanta (\approx \4\text{B}$)
- Charles Mourani: Co-Founder of Cherry (\approx \2\text{B}$)
- Kabir Barday: Co-Founder of OneTrust (\approx \4.5\text{B}$)
- Kunal Shah: CEO of WhatsApp; Co-Founder of CRED (\approx \4.5\text{B}$)
The financial impact of the waste Autostep targets can be modeled as:
🏢 Company Profile

Quick Facts:
- Founded: 2025
- YC Batch: P26
- Current Team Size: 1
- Status: Active
- Headquarters: San Francisco
Leadership:
Aidan Pratt 🥑 — CEO
# Conceptual Autostep Logic
def optimize_workflow(company_data):
waste_points = find_repetitive_tasks(company_data)
for point in waste_points:
cost = calculate_burn_rate(point)
if cost > threshold:
recommend_fix(point, "AI_Agent" or "Process_Change")
return "Efficiency Increased"