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About This Role
Company
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Tread is an AI\-native vertical SaaS platform transforming construction materials logistics. The company crossed $1Bn in monthly delivered load value in March 2026\. The platform serves Haulers, Producers, and Contractors, optimizing truck routing and delivery while providing customer service through AI agents. Tread is Series A\-funded by Mucker Capital.
Why This Role Exists
Tread's customers operate complex, high\-stakes logistics, and you can't onboard, support, or grow them from a desk. This is the customer\-side counterpart to our product\-side Forward Deployed Engineer: instead of shipping product code, you build and own the AI agents that run support, onboarding, and success, deployed alongside real customers with feedback loops measured in hours. As AI takes on the volume of customer work, this role lets us scale customer operations without scaling headcount. It sits in Customer Operations, reports to the VP of Customer Ops, and is measured on customer outcomes.
What You Own
- Agentic Support: Build the agents that resolve support end\-to\-end. Anything AI can answer gets answered by AI, with a human audit. First targets are our two highest\-volume drivers: login issues and multi\-driver phone\-number issues. Own the AI support layer (Fin\+ the Mintlify knowledge base behind it): decide what's safe to automate, validate against real prior conversations before go\-live, and keep the content trustworthy.
- Agentic Onboarding: Build end\-to\-end agentic onboarding flow, customized per customer. Vendor onboarding comes first, then product onboarding and case reporting. The agent tracks each customer's progress, delivers the right step at the right moment, and escalates only the stalls.
- Agentic Success \& Enablement: Map every post\-onboarding job to be done (QBRs, monthly check\-ins, renewal prep) and automate it, including voice\-based check\-ins. Build account\-health and usage monitoring that flags risk before a human would see it, plus self\-serve enablement that reduces hands\-on support.
- Expansion \& Retention Through Technical Depth: Solve customer problems preemptively, surface stickiness factors, and turn every recurring issue into a reusable agent so it never comes back to a human.
What Success Looks Like
First 90 Days
- Visit at least 3 customer sites and describe their workflows from memory
- Ship your first support automation against login \+ multi\-driver phone\-number issues
- Get Fin validated and live on a cleaned\-up knowledge base
- Take ownership of onboarding for 1 new customer, run agentically end\-to\-end
- Establish a baseline view of where human effort is being spent across support, onboarding, and success
First 6 Months
- One end\-to\-end vendor\-onboarding flow running agentically, with a human only on exceptions
- Post\-onboarding success jobs mapped and the first check\-in / health agent live
- Customer signals turn into shipped agent improvements in under a week
- Time\-to\-first\-value trending measurably downward
First Year
- A rising, defensible line on % of support, onboarding, and success work handled by agents
- Customers in your portfolio show measurably higher adoption and satisfaction
- You've defined the Customer\-Ops FDE playbook for future hires
- You're the trusted person customers rely on for the problems agents can't solve
What We're Looking For
- AI\-native builder. You automate your job away by instinct and reach for agents first. You're dangerous with APIs, scripts, SQL, prompts, and agent frameworks. You ship working systems without waiting on a full software engineer.
- Strong technical judgment. You can look at agent output or a dataset and know what's wrong, what's risky, and what's safe to release. Auditing AI is a core skill of this job.
- Customer\-facing instincts. You build trust with operations leaders and are comfortable owning a QBR with a CTO and a scrappy save with a dispatcher in the same day.
- Bias toward action without waiting for tickets; high ownership and low ego.
- Strong communication. You translate messy customer conversations into clear problem statements a system can solve.
- Willingness to travel occasionally for on\-site customer work.
- Analytical mindset. You instrument what you ship and let data decide the next step.
Bonus Points
- Construction technology, heavy civil, logistics, supply chain, or field\-services experience
- Prior support, implementation, CS, or solutions role you made dramatically more efficient with automation
- Experience building with LLMs / AI agents in real customer workflows
- Familiarity with our stack surfaces: Intercom, Linear, Omni, HubSpot, PostHog, and import/REST APIs
- Series A–C startup experience under 50 people in ambiguous environments
Compensation \& Location:
The anticipated base salary range for this role is $120,000–$160,000\. Final compensation will be determined based on factors including experience, skills, and geographic location.
While our preference is for candidates based in San Francisco, we are open to exceptional remote candidates within the United States.
Compensation Range: $120K \- $160K
Salary Context
This $120K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($140K) sits 36% below the category median. Disclosed range: $120K to $160K.
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.
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 $277,088 across 810 tracked positions. That's 27% 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 3,708 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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