Founding Algorithm Developer
About the role
Fleetline is hiring founding engineers to build and scale core optimization algorithms for production in the trucking industry. The role combines algorithm development, data pipelines, and customer-facing deployment in a fast-moving startup environment. In-office requirement in New York City.
Responsibilities
- 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
Qualifications
- 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
Skills
About Fleetline
Optimizing fleet planning with data driven algorithms
Fleetline is building the first complete-context algorithmic load planner for mid to large-sized trucking fleets. Today’s dispatchers face an overwhelming task: balancing regulations, customer demands, live fleet data, and individual driver needs, all while coordinating across siloed tools and teams. Mistakes run rampant, and each mistake is thousands of dollars. Even the select fleets using algorithms that have fleet-wide context struggle: the outputs are rigid, hard to interpret, and are blind to real-world nuances like driver preferences and schedule exceptions. Fleetline solves this by combining advanced optimization with LLMs that can easily adapt algorithms to each fleet’s needs and capture driver-specific information. The result is smarter planning truly optimized for every fleet and its drivers.
More open roles at Fleetline
- Founding Engineer11mo agoNew York, NY