Lead Software Engineer - Stealth AI Manufacturing Planning Startup

Location: Remote

Department: Portfolio roles

Location Type: REMOTE

Employment Type: FULL_TIME

• 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.

• 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.

• 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.