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About This Role
About Tread
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Tread is an AI\-native vertical SaaS platform transforming construction materials logistics—a massive, essential industry that moves the aggregate, asphalt, and concrete behind every road, bridge, and building. In July 2026, we crossed $2Bn in monthly delivered load value on the platform, and we're at \~$XM\+ ARR and growing fast.
Our AI\-powered platform serves Haulers, Producers, and Contractors—optimizing truck routing, loaded\-mile delivery, and project planning while delivering exceptional customer service through personalized AI agents.
We're a Series A company backed by Mucker Capital, and we're building the team that will take us from $XM to $XXM\+ ARR.
Why This Role Exists
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Tread has built a powerful company brain that is available for our internal team to power decision making and context setting, that helped power us to $XM in ARR. This is a unique and powerful asset that we believe can help us build a lean and incredibly fast AI driven finance operations practice. We believe that in extending our existing AI powered workflows, Tread can continue to scale revenue efficiently without a traditional Finance function. To get to $XXM\+ and beyond, we need a dedicated AI\-pilled finance professional who owns the entire revenue dollar journey from forecasting, modeling, biz\-ops, and some business development. This individual will build the function, extend the agents in place, and create the playbooks that help accelerate our journey to revenue growth and profitability.
We're deliberately looking for someone from an investment banking or growth equity / private equity background who wants to stop advising from the outside and start operating from the inside. You already know how to model a business, pressure\-test unit economics, and tell a crisp story to investors. Here you'll do all of that for a company whose numbers you actually own, sitting next to the founders as decisions get made.
What You'll Own
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### Strategic Finance \& FP\&A
- Own the model. Build and maintain the company's operating model, budget, and rolling forecast \- and make it the source of truth leadership runs the business on.
- Unit economics. Own the metrics that matter for a vertical SaaS \+ usage business: ARR, net revenue retention, CAC, payback, gross margin, magic number, and burn multiple. Identify where the economics break and which levers matter most.
- Decision support. Turn ambiguous questions \- pricing changes, new segments, hiring plans, go/no\-go on a big deal \- into clear, quantified recommendations the team can act on.
### Fundraising \& Investor / Board
- Board \& investor materials. Build the board deck, the data room, and the reporting package. Own the numbers investors see and be able to defend every one of them.
- Fundraising support. Partner with the CEO to run the next raise \- model, narrative, diligence, and the financial engine room that keeps a process moving fast.
- Cash \& capital. Own cash\-flow forecasting, runway, and scenario planning. Nobody at Tread should be surprised by the cash position.
### Business \& Revenue Operations
- Revenue operations. Connect the quote\-to\-cash workflow \- from deal to invoice to collections \- and make sure ARR is live, reconciled, and trustworthy.
- Operating cadence. Run the rhythms that keep a scaling company aligned: KPI dashboards, weekly business reviews, and the metrics definitions everyone agrees on.
- Cross\-functional leverage. Partner with GTM on comp plans and pipeline math, and with product/ops on the cost and margin side of how we deliver.
### AI\-Native Systems \& Automation
- Automate first, hire second. Design the finance and ops stack to run lean. Use AI tools \- Claude, ChatGPT, and the modern automation layer \- to handle reconciliations, variance narratives, data cleanup, reporting, and the busywork that eats an analyst's week.
- Build the systems. Own the tooling \- billing, ERP/accounting, CRM integrations, dashboards \- and wire them together so data flows without manual handoffs.
- Set the standard. Establish how a modern, AI\-first finance and ops function operates at Tread, and write the playbook for it.
Who We're Looking For
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We're looking for someone who is action oriented and high agency, and skilled in corporate finance and modeling. Specifically:
- 2–5 years in investment banking, growth equity, private equity, or a top\-tier strategic finance / corporate development seat.
- Elite financial modeling and analytical chops \- you can build a three\-statement model and an operating model from a blank sheet and defend every assumption.
- Commercial instinct on top of financial rigor \- you want to understand why the business wins.
- AI\-native. You already lean on AI tools to work faster, and you want to build a finance function around them. Comfort with AI is not optional here.
- Extreme ownership and comfort with ambiguity. There's no org chart or process here yet \- you'll build both.
- Clear, executive\-level communicator who can move between a founder, a board member, and a customer's controller in the same afternoon.
- No task is beneath you, and you'd rather ship the answer than hand off a recommendation.
### Nice to Have
- Exposure to SaaS or usage\-based business models and the metrics that go with them.
- Some accounting fluency (close, revenue recognition) or willingness to get fluent fast.
- Light technical skills \- SQL, spreadsheets\-as\-code, or hands\-on with automation tools (Zapier / Make / n8n).
- Interest in logistics, construction tech, or hard\-industry software.
Why This Role Is Rare
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- You own the whole thing. The model, the board materials, the cash, the systems \- the full scope a banker or investor usually only sees from across the table.
- Direct line to the CEO and the cap table. You'll sit in the room where decisions get made and help make them, from day one.
- A real launchpad. This is the seat that becomes Head of Finance, VP Finance, or Head of BizOps as Tread scales.
- Build the AI\-native version. Most finance functions are being retrofitted for AI. You get to build one that's AI\-native from day one.
- Real company, real numbers. Series A, real revenue, real enterprise customers, in a giant market that's barely been digitized.
What Success Looks Like (First 12 Months)
---------------------------------------------
- Leadership runs the business off your operating model, and the board trusts your numbers.
- Monthly close is fast, clean, and largely automated. Cash and runway are never a surprise.
- Unit economics are defined, instrumented, and understood across the team, and you've flagged the levers that matter most.
- The next fundraise (when we run it) has a finance engine room that makes diligence move fast.
- The finance \& ops stack runs lean and AI\-native, doing the work of a much larger team.
Compensation \& Benefits
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Competitive salary plus meaningful early\-stage equity \- this is a foundational hire and the equity matches it. Full health coverage and a flexible remote/hybrid setup.
How to Apply
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Send a note on why this role fits you to [email protected]. Tell us about a time you owned a number or a decision that mattered \- and, if you'd like, show us something you've built with AI. Tread is an equal\-opportunity employer. We hire for talent and drive, and we welcome applicants from every background.
Compensation Range: $140K \- $160K
Salary Context
This $140K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Tread, 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 Required
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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($150K) sits 30% below the category median. Disclosed range: $140K to $160K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Tread AI Hiring
Tread has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $160K - $160K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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