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About Burq
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.
Here’s a quick overview of what you will be doing:
The Role
We're looking for a Head of Data \& AI to build the intelligence that powers Burq.
You'll build a world\-class data\-and\-ML platform: the systems that turn data into models which make every delivery decision smarter and agents that automate the work the industry still does by hand. You'll set the strategy, make the hard architecture and modeling calls, ship models into production that move the business, and build the team to do it at scale.
This is a player\-coach role. You'll lead and grow the data and ML team while staying deeply hands\-on — making architecture calls, reviewing engineering and modeling work, and ensuring the platform serves both product and business goals. You'll partner closely with the Head of Engineering, and with product and AI leadership on how intelligence shows up in the product.
What You'll Do
Data Platform
- Own the full data platform — the warehouse, streaming and batch ingestion, the semantic layer, and BI infrastructure.
- Define and enforce data governance — access management, quality standards, and a well\-modeled, trustworthy "gold" layer.
- Make the warehouse the source of truth for operational, product, and financial analytics.
Intelligence \& Machine Learning
- Build the models that power delivery intelligence — forecasting, prediction, optimization, matching/allocation, and reliability scoring that turn our data into better real\-time decisions.
- Stand up the ML systems that make this real and reliable: feature pipelines, training, serving, evaluation, and drift monitoring.
- Design the learning loop — instrument decisions and outcomes so models continuously improve.
- Drive the AI agent initiative to production, partnering with product and engineering on the surrounding workflows.
Team \& Stakeholders
- Manage and grow the data and ML team (starting from a small core) — hire data engineers, analytics engineers, and data scientists/ML engineers as the platform matures.
- Establish a clear intake process for ad hoc data requests so the team works from a roadmap, not in reactive mode.
- Partner with Sales, Customer Success, and Finance on data\-driven analyses and decisions.
Requirements
- 8\+ years in data/ML, including 2\+ years in a leadership or staff\-level role owning a data or ML platform.
- Player\-coach — you lead a team and still go deep in the technical work: architecture, modeling, and code review.
- Strong on both halves of the role:
- + Data platform — modern data warehouse design (e.g., Snowflake/BigQuery), streaming \+ batch ingestion, dbt or equivalent, performance and cost optimization, governance.
+ Machine learning — you've built and shipped production models (forecasting, optimization, ranking/matching, or prediction) with real business impact, plus the ML systems around them (feature pipelines, serving, monitoring/MLOps).
- Experience building data and ML products, not just pipelines — you understand how data and models serve product and business goals.
- Familiarity with modern AI/LLM and agentic data work is a strong plus.
- Strong stakeholder management — you can say no to an ad hoc request with a good reason and a better alternative.
- Experience at a high\-growth startup or scale\-up where the platform was built, not inherited.
Location
This is a fully remote role, open to candidates based in the United States and Canada, with a few in\-person offsites throughout the year.
How We Work
We're remote with a few in\-person offsites throughout the year. We move quickly, default to writing, and ship early to real customers before rolling things out broadly. AI is part of how we work every day, not a separate initiative — we expect people to experiment with new tools, share what they learn, and continuously rethink how great products get built. We care more about speed, ownership, and iteration than process for the sake of process.
Benefits
Investing in you
- Competitive salary, stock options, and performance\-based bonuses
- Fully remote
- Comprehensive medical, vision, and dental insurance
At Burq, we value diversity. We are an equal opportunity employer: we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Burq, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Burq AI Hiring
Burq has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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