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Head of Engineering - AI Logistics Billing Platform Remote Portfolio roles Remote Full-time Company Overview:
Groundtruth, a Vantora (formerly UP.Labs) portfolio company is hiring a hands-on Head of Engineering. Groundtruth is building an AI-powered platform focused on freight billing, revenue integrity, and cash-flow automation.
Freight billing today is fundamentally broken. Complex contracts, fragmented systems, and manual, error-prone workflows lead to disputes, revenue leakage, delayed payments, and significant overhead. This company exists to change that by unifying fragmented data into a single source of truth, validating invoices automatically, and identifying discrepancies in real time, turning revenue chaos into predictable outcomes.
As the first engineering leader, you will own the technical foundation end-to-end: architecture, data infrastructure, AI integration, and team building. You’ll stay hands-on early, build from first principles, and define the long-term technical direction as the platform scales to enterprise adoption.
You’ll also have access to the UP.Partners ecosystem, a world-class team across product, engineering, design, analytics, marketing, legal, talent, finance, and venture capital - to help accelerate execution.
In This Role, You Will:
- Lead and grow the engineering function, balancing hands-on development with technical leadership and team building.
- Architect and own the core platform, including: + Data ingestion and normalization pipelines + AI/ML and rules-based validation layers + Scalable, secure enterprise infrastructure
- Build and maintain deep integrations with enterprise systems such as TMS, ERP, billing platforms, and other operational data sources.
- Partner closely with Product and Data Science to translate complex, real-world billing workflows into intelligent, automated solutions.
- Design systems that support invoice validation, discrepancy detection, exception handling, and auditability.
- Establish best practices around security, CI/CD, observability, testing, and documentation suitable for enterprise finance and operations teams.
- Define the technical vision for AI-driven automation, explainability, and closed-loop resolution workflows.
- Manage and mentor distributed contributors (including nearshore/offshore teams), building a high-trust, high-output engineering culture.
- Deliver rapid MVP iterations while laying a durable foundation for enterprise scale and reliability.
Who You Are:
- A technical leader who thrives at the intersection of data, AI, and enterprise SaaS.
- Deeply hands-on comfortable writing code, reviewing PRs, and making architectural tradeoffs.
- Experienced integrating complex, messy enterprise systems and normalizing inconsistent data.
- Fluent in building platforms that combine data infrastructure, applied machine learning, and workflow automation.
- A collaborative partner who works seamlessly with product, data science, and business stakeholders.
Comfortable operating in ambiguity and scaling systems and teams from 0* 1 and beyond.
You Should Have:
- 8+ years of software engineering experience, including 4+ years leading small, high-impact teams.
- Strong backend or full-stack background with the ability to remain hands-on.
- Proven experience building and scaling B2B SaaS platforms with complex data pipelines and system integrations.
- Deep knowledge of cloud architecture, CI/CD pipelines, observability, and distributed systems.
- Experience working with AI/ML systems (e.g., LLMs, rules engines, optimization or validation models).
- Familiarity with enterprise finance, billing, or revenue systems is a strong plus.
- Experience leading or managing distributed (nearshore/offshore) engineering teams.
- Background in logistics, supply chain, or fintech is helpful but not required.
Why Join:
- Solve a high-impact, revenue-critical problem for logistics operators using AI and automation.
- Build a category-defining platform at the intersection of logistics, finance operations, and applied AI.
- Own the technical foundation from day one with real influence over architecture and strategy.
- Work closely with top enterprises, operators, and investors through the UP.Labs model.
- Meaningful equity ownership in a high-growth venture with enterprise-scale potential.
About Vantora: Vantora builds high-growth technology startups that enable faster, cleaner, and safer movement of people and goods. Our platform is unique in three ways:
- Risk: We reward our team and partners with meaningful equity.
- Technology: We build and launch scalable technology products from day one.
- Industry Focus: We stay deeply focused on the underlying fabric of mobility and logistics.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At UpLabs, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
UpLabs AI Hiring
UpLabs has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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