A firm of puzzle solvers building the future of trades
Dispatch requires a lot of math and reasoning. We build models that aggregate finances and power better routing for the trades, working closely with private equity across the industry. We're looking for exceptional people willing to solve challenging problems with cutting-edge research.
Rigorous + pragmaticThe right tool for the problem
Rooted in the tradesWorking closely with private equity in trades
How we work
Rigorous and pragmatic
We work together to train models, architect systems, and run strategies. Depending on the day, we might be diving deep into tuning hyperparameters, debugging distributed training performance, or studying how our model likes to operate. If you have a curious mind and a passion for solving interesting problems, we have a feeling you'll fit right in.
A firm of puzzle solvers
We apply logical and mathematical thinking to every kind of problem — and enjoy the ones that don't have an obvious answer.
Serious scale
We run large clusters to build models that aggregate finances and power better routing across the trades.
Curious & precise
Open-minded thinkers and precise communicators who ask questions, admit mistakes, and learn new things.
Open roles
Come solve hard problems
Every role is based in San Francisco and in person. Most candidates will have experience with data science or machine learning, though your approach to problem-solving and capacity to learn will weigh just as heavily as your existing expertise.
Customer Success Lead
$120k–$180k + 0.5%
Customer SuccessSan FranciscoIn personFull-time
Own the success of the plumbing and HVAC teams that run on Fleety. You'll lead onboarding, drive adoption, and turn operator feedback into product direction — making sure every customer sees real revenue impact from the day they start.
You should be:
An open-minded thinker and precise communicator who enjoys collaborating with colleagues from a wide range of backgrounds and areas of expertise
Intellectually curious; eager to ask questions, admit mistakes, and learn new things
Comfortable translating between technical teams and non-technical operators
Energized by owning outcomes and moving quickly with customers
We're a firm of puzzle solvers looking for Quantitative Researchers to help us build models, strategies, and systems that aggregate finances and power better routing for the trades. You'll apply your experience in experiment design, dataset generation, time series analysis, feature engineering, and model building to work together to train models, architect systems, and run strategies on our large clusters. Depending on the day, we might be diving deep into tuning hyperparameters, debugging distributed training performance, or studying how our model likes to operate.
You should be:
Able to apply logical and mathematical thinking to all kinds of problems
Intellectually curious; eager to ask questions, admit mistakes, and learn new things
A strong programmer who's comfortable with Python
An open-minded thinker and precise communicator who enjoys collaborating with colleagues from a wide range of backgrounds and areas of expertise
Dispatch requires a lot of math and reasoning. As a founding engineer, you'll work together with the team to train models, architect systems, and run strategies on our large clusters — building models that aggregate finances and power better routing for the trades. Depending on the day, we might be diving deep into tuning hyperparameters, debugging distributed training performance, or studying how our model likes to operate.
You should be:
Able to apply logical and mathematical thinking to all kinds of problems
Intellectually curious; eager to ask questions, admit mistakes, and learn new things
A strong programmer who's comfortable with Python
An open-minded thinker and precise communicator who enjoys collaborating with colleagues from a wide range of backgrounds and areas of expertise
We're looking for an engineer with robust experience in machine learning and strong mathematical foundations to join our growing ML team and help drive the direction of our ML platform. Our ML team is full of people with a shared love for the craft of software engineering, and for designing APIs and systems that are delightful to use. We'll rely on your in-depth knowledge of the ML ecosystem — whether it's neural networks, random forests, gradient-boosted trees, or sophisticated ensemble methods — to aid decision-making so we apply the right tool for the problem at hand. Your work will also focus on enhancing research workflows to tighten our feedback cycles. Successful ML engineers will be able to understand the mechanics behind various modeling techniques, while also being able to break down the mathematics behind them.
We're looking for someone with:
Experience building and maintaining training and inference infrastructure, with an understanding of what it takes to move from concept to production
A strong mathematical background; good candidates will be excited about things like optimization theory, regularization techniques, and linear algebra
A passion for keeping up with the state of the art — diving into academic papers, experimenting with the latest hardware, or reading the source of a new machine learning package
A proven ability to create and maintain an organized research codebase that produces robust, reproducible results while maintaining ease of use
Expertise wrangling an ML framework — we're fans of PyTorch, but we'd also love to learn what you know about Jax, TensorFlow, or others
An inventive approach and the willingness to ask hard questions about whether we're taking the right approaches and using the right tools
