Staff Machine Learning / Operations Research Engineer

About Burq

We're revolutionizing commerce from port to doorstep with Burq Intelligence.
Burq started with an ambitious mission: to turn the complex process of offering delivery into a simple, turnkey solution. It's a big mission, and now we want you to join us in making it even bigger.
We're proud to be recognized as one of Fast Company's Best Workplaces for Innovators and a 2025 Inc. Magazine Power Partner, awards that highlight how we're redefining the future of logistics while empowering our partners to grow.
Backed by leading Silicon Valley investors like Village Global, the fund whose investors include Bill Gates, Jeff Bezos, Mark Zuckerberg, Reid Hoffman, and Sara Blakely, we've built a world-class team across the globe.
We operate at scale but remain small enough for every person to have a massive impact. There's a lot of important work ahead, and joining Burq means the opportunity to grow faster than ever while doing the most meaningful work of your career.
The role
We're hiring a Staff ML/OR engineer to define and build the intelligence at the core of Dispatch OS, our platform for ML-assisted dispatch decisions. You'll set the technical direction for how Burq prices, selects, forecasts, and routes deliveries, and you'll personally build the highest-leverage models and optimization systems behind it.
You'll own the architecture and roadmap for ML and optimization across the platform, make the key technical bets, and raise the bar for how we build, evaluate, and ship models. This is a hands-on IC role with company-level impact on revenue, margin, and delivery reliability.
What you'll do
Technical leadership

Technical direction: own the ML and optimization roadmap for Dispatch OS, deciding which problems get ML, which get solvers, which stay heuristic, and in what order

Architecture: design the end-to-end architecture for model serving, evaluation, and optimization so it scales with order volume, new providers, and new customers

Hardest problems: lead the most ambiguous, high-stakes modeling and optimization problems from framing through production, and guide technical work across Engineering and Data

Raise the bar: set standards for experimentation, evaluation, and production ML, and mentor engineers through design reviews, pairing, and code review

Strategic partnership: work with Product and leadership to shape product strategy and identify where ML and OR create competitive advantage

Core systems

Quote selection and pricing: design and ship models for quote selection, dynamic pricing, and reliability scoring that measurably improve margin, win rate, and conversion

Forecasting: build demand and volume forecasting models, including modern deep learning approaches where they outperform classical methods, to - help operations and customers plan driver and fleet capacity

Optimization: move dispatch decisions from heuristics to solver-based optimization for batching, route optimization, and vehicle/fleet recommendation, using OR-Tools, Gurobi, or similar where it earns its keep

LLMs and agents: apply LLMs and AI agents to dispatch workflows, such as extracting provider rules and rate terms from documents, and automating quote follow-ups and exception handling

Evaluation and trust: build rigorous, replayable evaluation frameworks that let customers verify a model's performance against their own historical decisions before they trust it to act

Constraint translation: turn real operational constraints (driver availability, provider rules, cost ceilings) into model requirements, scoring logic, and optimization formulations

MLOps: design and own automated pipelines for training, deployment, and monitoring so models stay accurate in production at scale

AI-native work: use AI tools daily to speed up experimentation, automate eval pipelines, and debug faster

Requirements
Requirements

9+ years in applied ML or ML engineering, including multiple years operating at the senior or staff level, with models shipped to and maintained in production

Track record of setting technical direction for ML or optimization systems, where architecture decisions you made shaped a product or platform over multiple years

Demonstrated ability to lead complex technical initiatives across teams without direct authority

Track record of ML or optimization systems with quantified, company-level business impact (e.g., tens of millions in revenue, or major utilization or margin gains), ideally in pricing, logistics, marketplaces, or operations

Deep experience with decision, ranking, and scoring problems where model outputs directly drive a business action

Strong quantitative and algorithmic reasoning, including combinatorial problems, constraint satisfaction, and algorithm design

Hands-on experience formulating and solving optimization problems (LP/MIP, constraint programming, or VRP-style routing)

Experience with time-series forecasting in production

Hands-on experience deploying LLM-based systems in production, such as fine-tuned models, extraction pipelines, or agents

Experience owning end-to-end ML pipelines and MLOps (training, deployment, monitoring, retraining)

Comfortable with messy, incomplete, constraint-heavy operational data, and able to build models that honor hard business constraints rather than treating them as soft penalties

Experience building evaluation frameworks that non-technical stakeholders can understand and trust

Nice to have

Experience with delivery/dispatch software, TMS platforms, or routing systems

Production experience with commercial or open-source solvers (OR-Tools, Gurobi, CPLEX)

Pricing or revenue management experience in aviation, fleet, or transportation

Published work, patents, or open-source contributions in ML, OR, or pricing

Benefits
Investing in you 🙏

Fully remote

Medical, vision, and dental insurance

Reimbursement for educational courses

Generous time off

Originally posted on Himalayas