Lead Software Engineer - Stealth AI Manufacturing Planning Startup at UP.Labs

, , United States -

Full Time

Start Date

Immediate

Expiry Date

27 Sep, 26

Salary

0.0

Posted On

29 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Node.js, Python, Azure Databricks, System Design, Backend Architecture, B2B SaaS, Optimization Algorithms, Demand Planning, Production Scheduling, Engineering Leadership, JavaScript, Cloud Data Platforms, AI/ML Integration, Operations Research, Scaling Infrastructure, Technical Mentorship

Industry

Venture Capital and Private Equity Principals

Description

About the Role: UP.Labs is seeking a proven Lead Engineer with a track record in 0>1 environments to join a new AI platform in our portfolio. This stealth venture is reimagining how mid-market manufacturers manage production planning and scheduling- an area still dominated by spreadsheets, MRP noise, and manual firefighting. Built in partnership with a $1.5B+ truck body manufacturer, the platform is a demand-driven planning co-pilot that helps manufacturers reduce schedule volatility, improve on-time delivery, and scale operational intelligence across their plants. This is not a role for someone looking to maintain or inherit a system. You’ll join as one of the earliest technical hires, working directly with the CTO to design and build core infrastructure from the ground up, and eventually grow into leading the engineering team as the company scales. What You’ll Do: * Own backend architecture and system design decisions alongside the CTO - whiteboard to production. * Build and scale the core platform on a Node.js / Python stack with Azure Databricks as the data layer. * Design and implement optimization-heavy algorithms for demand planning and production scheduling. * Establish engineering patterns, standards, and discipline as the team grows from its earliest stage. * Collaborate directly with product and data functions - the team is small, the surface area is wide. * Mentor and eventually lead a small team of engineers, setting the technical bar without the ego. * Use AI as a force multiplier across development and product capabilities. How You’ll Operate: * You rose through the ranks at a startup, not a big company. You built things from scratch and learned system design by doing it. * You think architecturally, not just tactically - when complexity increases, you bring discipline to it, not workarounds. * You’ve scaled a B2B SaaS product to real customer loads and understand what that actually requires: security, capacity, reliability. * You lead without pretension - deep technical expertise without needing a title to prove it. * You’re comfortable operating in ambiguity early on, wearing multiple hats, and building the plane while flying it. You Should Have: * 5–10+ years of engineering experience, with meaningful tenure at startups - ideally founding or early engineer who stayed and saw a product scale. * Demonstrated 0>1 experience on a B2B SaaS product, with firsthand exposure to scaling to thousands of customers. * Proficiency in Node.js and Python; comfort across a modern JavaScript stack. * Experience with Azure Databricks or comparable cloud data platforms. * Some experience leading or mentoring engineers, even informally. * Startup grit: willing to operate solo or in a very small team and own outcomes end-to-end. * (Nice-to-have) Experience building or working within AI/ML-integrated stacks. * (Nice-to-have) Exposure to optimization algorithms, operations research, or scheduling systems. About UP.Labs: UP.Labs builds high-growth tech startups that enable faster, cleaner, and smarter movement of people and goods. Our unique approach combines meaningful equity for team members, scalable software built from the ground up, and deep corporate partnerships with industry leaders invested in the outcome. We focus on the first year of a venture’s lifecycle - from ideation to MVP and beyond.

Responsibilities

Design and build the core backend infrastructure and optimization algorithms for an AI-driven manufacturing planning platform. Establish engineering standards and lead the technical team as the company scales from MVP to production.