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Company
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Tread is the AI\-native operating system for the $660B construction materials logistics market: the aggregate, asphalt, and concrete behind every road, bridge, and building in the country. Our customers run the gamut: enterprise producers and contractors that supply the world's largest infrastructure projects, alongside the family\-owned hauling fleets that have moved the material this work depends on for generations. They're the operators who actually build the roads, bridges, and buildings around us. Most of the work still happens on paper tickets, phone calls, and disconnected scale houses. We're building the software they run on.
We crossed $1Bn in monthly delivered load value on the platform in March 2026\. We’re at \~$XM ARR, and growing fast. Our enterprise customers reconcile real money, real freight, and real P\&L against our data every day. Tread is the most important software they use.
We’re a Series A company backed by Mucker Capital.
We’re in growth mode. If you are fulfilled by ownership and a life\-changing outcome, let’s connect.
Role
Tread is building a Customer Ops team that can own the full technical customer relationship — from complex support issues to high\-stakes escalations — without pulling Product and Engineering into every hard problem. As our first senior technical hire in this function, you'll work directly in the queue, step into accounts when technical complexity or risk demands it, and build the AI\-powered systems and standards the team runs on. Critically, you'll be the bridge between what customers are experiencing and what Product and Engineering actually need to hear — keeping the noise out and making sure what reaches them is clear, prioritized, and worth their time.
We win by protecting Product's time and delivering real outcomes for our customers. That requires someone who can do both the hands\-on work and the systems thinking — not one or the other. If you thrive on hard technical problems, care deeply about quality, and want to build something that compounds, this role is for you.
Your Impact
- Own the Hard Problems: Work the support queue on the most complex and escalated issues, and turn every hard case into reusable knowledge for the team
- Be the Technical Resource on Accounts: Step into high\-ARR or at\-risk accounts when they need complex technical support or de\-escalation — partner with CSMs to stabilize relationships and solve what the frontline can't
- Raise the Escalation Bar: Define and enforce triage standards for what reaches Product and Engineering — and send incomplete escalations back with guidance until the standard sticks
- Be the Bridge to Product \& Engineering: Build a trusted, high\-signal relationship with Product and Engineering — acting as the filter that keeps noise out, and ensuring what reaches them is scoped, prioritized, and grounded in real customer impact
- Turn Noise into Signal: Convert messy customer feedback into structured, ARR\-weighted product input that Product can actually act on
- Enable the Team: Build the playbooks, decision trees, and documentation that help CS and Support work more independently — and coach them to get there
- Build with AI: Deploy AI\-assisted triage, bot flows, and automation to deflect repeat volume, accelerate resolution, and make the team more capable at scale
What Success Looks Like
First 90 Days
- Fully embedded in the support queue and handling escalations independently
- Escalation triage standards defined and being held by the team
- First AI workflow or automation shipped
First Two Quarters
- Measurable reduction in escalations reaching Product and Engineering
- Playbooks and decision trees built and in active use by the CS and Support teams
- High\-ARR accounts stabilized where you've been involved
What You'll Bring
- 4–7 years in technical support, technical customer success, or solutions engineering at a B2B SaaS company
- Direct hands\-on experience in a support queue — this is not a purely strategic role
- Experience navigating technically complex or at\-risk customer situations, including escalations and de\-escalation
- Experience building a productive working relationship with a Product or Engineering team — you understand what makes their time worth protecting
- Hands\-on experience building escalation processes and coaching others to hold the standard
- Functional comfort with APIs, webhooks, JSON/CSV, and basic SQL — enough to investigate issues without Engineering
- Experience using AI tools to improve support workflows, automate repetitive work, and drive team output
- Sharp judgment: knows the difference between a bug, a workflow issue, a training gap, and a product gap
- Comfortable working with data and supporting complex customer\-facing reporting, analytics, and reconciliation requests
- Experience supporting technically complex customer implementations, onboarding, integrations, and go\-live activities
- Able to translate technical concepts and data into clear recommendations for both customers and internal stakeholders
- Spanish\-speaking is a plus given our customer base
Why Tread
- Build the function from the ground up — the playbooks, the standards, and the team
- Work closely with Product and Engineering leadership to define what great customer operations looks like
- Massive, underserved market with real customer pain and measurable ROI
- High ownership and direct influence on how we scale
- Clear path from player\-coach to leading the team you helped build
Compensation:
$120,000–$150,000 base salary, plus equity. Compensation will be determined based on experience, skill set, and alignment with the role.
Compensation Range: $120K \- $150K
Salary Context
This $120K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($135K) sits 25% below the category median. Disclosed range: $120K to $150K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
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: $150K - $150K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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