AI Data Engineer

Staff AI Data Engineer

About Work Truck Solutions

The Opportunity

How You'll Spend Your Time

• ~60% productizing data. Building models, analyses, and data products that reach customers—forecasting, pricing signal, recommendations, matching, enrichment, market intelligence—and iterating on them based on how they actually get used.
• ~25% AI-driven capability. Applying LLMs and ML to extract, structure, and enrich the data that makes those products possible, with the evaluation rigor to know it's working.
• ~15% data engineering. Building the ingestion, transformation, and feature pipelines your work depends on, and setting standards others can follow.

Key Responsibilities

• Own customer-facing data products end to end: define the opportunity, build the model or analysis, ship it, measure whether it actually helped, and iterate.
• Build the intelligence layer of our platform—demand forecasting, pricing and market signal, inventory and configuration recommendations, matching and ranking—on problems where being right has direct commercial consequence for our customers.
• Partner with product and design on how model output surfaces to a dealer or upfitter, what happens when it's wrong, and how much confidence to express.
• Work directly with customers and the commercial team to understand what decisions they're actually trying to make, and let that shape what you build.
• Define and instrument success metrics for everything you ship; be the person who knows whether it worked.

• Apply LLMs to the unstructured layer of our business: extraction from spec sheets and vehicle descriptions, classification, taxonomy mapping, enrichment, semantic search and matching.
• Build the evaluation infrastructure that makes AI output trustworthy—golden datasets, offline and online metrics, monitoring for degradation, and guardrails with sensible fallback behavior.
• Bring AI into your own workflow aggressively and critically, and raise the practice of the people around you.
• Make honest calls about where generative approaches beat classical ML or plain deterministic logic, and where they don't.

• Design and build the ingestion, transformation, and feature pipelines your models depend on, rather than waiting for them.
• Contribute to entity resolution and normalization systems that turn inconsistent supplier data into a trustworthy canonical record.
• Establish data quality, lineage, and contract standards for the data your products rest on.
• Partner with data engineering on the platform decisions that outlast any single project, and hand off what you build in a state others can own.

• Set the standard for analytical and modeling rigor through peer review, and mentor the analysts and engineers around you.
• Write clearly enough that your findings change decisions and your systems can be maintained by someone else.

Qualifications

• 8+ years applying data science to real problems, with a track record of models and analyses that shipped to users and changed outcomes—not internal reports that circulated and stalled.
• Demonstrated ownership of customer-facing data products, where your model's output was the product and its quality was visible to people paying for it.
• Strong statistical foundation: experimental design, regression, uncertainty quantification, and the judgment to know what your assumptions are and what happens when they break.
• Substantial applied modeling experience across the families that matter here—forecasting, recommendation and ranking, gradient boosting, segmentation, entity resolution and fuzzy matching, anomaly detection.
• Genuine rigor about evaluation: offline metrics that predict online behavior, correct validation for temporal and grouped data, leakage awareness, and calibration—not just accuracy.
• Product instinct. You care whether the customer's decision got better, not whether the model was interesting.

• Hands-on production experience applying LLMs to data problems: structured extraction, classification and enrichment, embeddings for similarity and clustering, semantic search and matching over messy real-world text.
• Experience building evaluation and guardrail systems for probabilistic output; you don't ship a prompt without a way to know when it degrades.
• Working command of the production tradeoffs—model selection, structured output enforcement, context and token cost, latency, caching, human-in-the-loop review—and able to build a business case that accounts for them.
• Actively curious about the frontier of these tools and eager to apply them, paired with the discipline to verify rather than assume.
• Familiarity with agentic patterns and tool use, with a realistic view of where they're production-ready and where they aren't.
• The judgment to argue against AI when a well-chosen heuristic or a clear dashboard solves the problem more cheaply and more legibly.

• Expert SQL and strong Python; you write production code that others maintain comfortably.
• Able to design and build ingestion and ETL/ELT independently—orchestration and transformation tooling, batch and streaming patterns, and sensible data modeling.
• Experience in a modern warehouse or lakehouse environment (BigQuery, Snowflake, Databricks, or equivalent), including awareness of cost and performance.
• Comfortable integrating messy, semi-structured, unreliable third-party sources and building the reconciliation that makes them usable.
• Version control, code review, CI/CD, and reproducible work. Your output is not a folder of untracked notebooks.

• Exceptional written communication. At this level, the writing is part of the deliverable.
• Demonstrated influence without authority across product, engineering, and commercial teams.
• Comfortable scoping a vague business question into tractable work without being handed the framing.
• Bias toward shipping and learning, with the discipline to follow through past launch.
• Bachelor's degree in a quantitative field, or equivalent depth demonstrated in practice. Advanced degrees welcome but not required.
• Currently resides in one of the following states: CA, TX, FL, MN

Nice to Have

• Experience in automotive, dealership software, logistics, supply chain, fleet, or industrial B2B.
• Pricing, demand forecasting, or inventory optimization background.
• Background with catalog, taxonomy, or configuration data at scale.
• Marketplace experience: liquidity, matching efficiency, supply and demand balance.
• Experience building a company's first customer-facing data product rather than inheriting a mature one.

Why Join Us?

• Remote Flexibility: Work from anywhere in the U.S. while staying connected to a collaborative team.
• Impactful Work: Contribute to products that are reshaping the commercial vehicle industry.
• Growth Opportunities: Be part of a rapidly growing company with ample opportunities for professional development.
• Inclusive Culture: Join a team that values diversity, creativity, and innovation.