Fleetline writes optimization algorithms for large trucking companies. After a recent spike in contracted customers, we’re expanding the team and hiring founding engineers.

A bit about us

• ~$500k in contracted ARR within 3 months of launch, with a clear path to $1m in the next 6 months
• Founded by two best friends from high school
• Lean, welcoming, and highly aligned founding team; everyone is expected to care deeply about the vision and each other
• Backed by top investors including BoxGroup, Base10, Bloomberg Beta, and Y Combinator

Trucking is a trillion-dollar industry where dispatchers make thousands of interdependent mathematical decisions every day. Why algorithm developers can be massively impactful in this industry should be self-evident.

About the role

• Build and scale core optimization algorithms used directly in production
• Write optimization systems (e.g. MILPs, LPs, heuristics, etc.)
• Build and maintain data pipelines that operate at real production scale
• Move quickly from idea → implementation → customer usage
• Contribute to long-term product and technical direction
• Willingness to do whatever is needed technically, from deep algorithm work to integration and tooling
• Comfortable being forward-deployed: talking to customers, understanding constraints, and solving concrete problems
• In office in New York City is required.

What we’re looking for

• Has experience writing optimization algorithms (MILPs, ML, DP, RL, stochastic models, heuristics, or similar)
• A generalist who can operate across the stack when needed
• Ships code fast and often; not a perfectionist
• Comfortable working in ambiguity with incomplete information
• Extremely hardworking when part of a strong, mission-aligned team
• Background may include top startups or big tech, ex-founder / founding engineer experience, or serious side projects that are shipped and live
• Comfortable with Python and TypeScript

Pedigree is not important. We’re looking for people who are intelligent, gritty, curious, and very good at getting real systems into the world.