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About This Role
First Student is North America's leading provider of student transportation, helping millions of students get to and from school safely each day. Our technology teams build and support the operational, safety, customer, and employee\-facing systems behind that work, including our HALO platform.
AI is an area of active investment for us, and this role is one of the ways we are building AI capabilities the business can rely on and govern.
About the Role
You will own First Student's path to production for agent\-built software. Supervisor here refers to owning that route, the same way an Agentic Software Supervisor owns code generation. The role has no direct reports. You decide how code moves from an AI Software Supervisor's environment into production, you supervise the agents to the automated route that carries it there, and you own how it behaves once it is live.
This role is one seat in a three\-person pod we are building toward. The other two are a Product Builder, who decides what to build and why, and an Agentic Software Supervisor, who directs coding agents to build it. Your part is making sure what they produce reaches production fast, holds up once it is there, and never puts student data or daily operations at risk. We are still standing the pod up, so depending on when you join, some of these seats may be filling in around you, and you will have real say in how the pod works and how the path to production gets built. The problem stays the same either way: the pod can write working code far faster than a conventional team, and that speed only pays off when the code reaches production safely. Closing that gap is the job.
Your results show up in two numbers: how fast code reaches production, and how safely. The path has to keep up with the AI agents generating code, what ships has to be reliable, and a three\-person pod has to deploy and operate like a much larger one.
The role consolidates work that used to be spread across release engineering, CI/CD, infrastructure, QA automation, and site reliability, and puts it under one owner of the route to production. In market terms the closest match is an AI\-era platform engineer. You will build in our standard stack of React, React Native, and AWS. Because much of what ships touches student data, FERPA and enterprise governance are part of the work.
How You'll Work
You will use AI agents and tools to build and run the delivery path itself, not only to ship the product code that travels down it. Deployment automation, test and evaluation harnesses, policy checks, and monitoring are things you assemble and keep improving, the same way the Agentic Software Supervisor works with code generation. Your job is to define what counts as production\-ready here, prove it automatically, and decide where a human still has to sign off.
A central part of the role is building the paved road: the golden paths, guardrails, and self\-service steps that let the pod ship without re\-deciding how deployment, testing, security, and rollback work every time. The goal is a route where the safe way to production is also the fast way, so no one has a reason to go around it.
A typical week may include:
- Designing and building the automated path that carries agent\-generated code from merge to production, with the gates and checks that keep it safe at agent speed.
- Standing up or extending CI/CD, infrastructure\-as\-code, and deployment automation on AWS.
- Building automated quality, security, and policy gates that catch defects in AI\-generated code before they reach production.
- Operating deployment, test, and monitoring agents, and reviewing their output, so the path runs with little manual effort.
- Instrumenting production for observability, and owning rollback, incident response, and recovery when something breaks.
- Working with the Product Builder and Agentic Software Supervisor to close the gap between code being written and code being live.
Key Responsibilities
Own the path to production
- Work closely w/ Dev manager \& Cyber leader
- Decide and document how code moves from an AI Software Supervisor's environment into production, and own that route end to end.
- Build and maintain CI/CD pipelines, infrastructure\-as\-code, and deployment automation that ship reliably on AWS.
- Make deployment fast and repeatable through progressive delivery (canary, blue/green, feature flags) so changes ship in small, reversible steps.
- Keep the path ahead of the rate coding agents generate code, clearing manual bottlenecks as volume grows.
- Own release readiness and what actually ships, beyond the mechanics of the pipeline.
Build and operate the agentic delivery platform
- Build the golden paths, scaffolds, and self\-service workflows that let the pod deploy without re\-solving deployment each time.
- Use AI agents and tools to set up and run the delivery process, including deployment, testing, and monitoring agents that you configure and validate.
- Encode standards into the platform as reusable defaults and automated checks, so the right way is built in instead of written down and forgotten.
- Treat the delivery platform as a product: track adoption, gather feedback from the builders who use it, and improve it over time.
- Build for more than today's pod, so the path can support additional builders and teams as agentic delivery spreads across IT.
Keep production safe, fast, and observable
- Build automated quality gates (unit, integration, end\-to\-end, contract, and evaluation checks) that verify AI\-generated code at machine speed.
- Encode security, privacy, and compliance into the path as policy\-as\-code and safe defaults, so the obligations attached to student and driver data, including FERPA, hold by construction.
- Manage secrets, access, and least\-privilege provisioning across environments, including for the agents operating inside the pipeline.
- Instrument production with logging, metrics, and monitoring so problems surface early and are easy to diagnose.
- Own rollback, incident response, and recovery, and drive down both how often changes fail and how long recovery takes.
- Partner with architecture, security, and AI governance to keep the path aligned with enterprise standards.
Required Qualifications
- 5\+ years building and operating production delivery systems (CI/CD, infrastructure\-as\-code, deployment automation, or platform/SRE work), with senior\-level judgment about what is safe to ship.
- A track record of shipping software to production reliably and repeatably, and owning what happens after deploy.
- Strong infrastructure\-as\-code and cloud deployment skills, ideally on AWS (for example Terraform or CDK, containers, and managed compute).
- Strong automated testing and quality\-gate practices, including using tests and checks to validate code you did not write by hand.
- Practical experience with observability, monitoring, incident response, and rollback in production.
- Experience building security and compliance into delivery: secrets management, least privilege, and policy or guardrail enforcement in pipelines.
- Demonstrated experience using AI coding agents or AI\-assisted tools to ship real work, beyond autocomplete or experimentation.
- Ability to break a delivery process into clear, automatable steps and build the automation that runs it.
- Experience in enterprise environments with security, governance, data\-handling, and production\-support constraints.
- Strong written and verbal communication, including explaining delivery tradeoffs and risk to technical and non\-technical stakeholders.
- Bachelor's degree in Computer Science, Engineering, a related field, or equivalent practical experience.
Preferred Qualifications
- Direct experience deploying React and React Native applications on AWS.
- Experience with AWS delivery and AI services such as CodePipeline, CodeBuild, ECS/Fargate, Lambda, API Gateway, Step Functions, S3, DynamoDB, RDS, or Bedrock.
- Experience building internal developer platforms, golden paths, or paved\-road workflows (for example Backstage or equivalent).
- Experience with progressive delivery (canary, blue/green, feature flags) and supply\-chain security (SBOMs, artifact signing, dependency scanning).
- Experience with policy\-as\-code (for example OPA) and compliance\-by\-construction in regulated environments.
- Experience building evals, guardrails, monitoring, or quality gates for AI\-generated or AI\-embedded systems.
- Experience with advanced testing such as contract, property\-based, or mutation testing.
- Experience with geospatial, routing, logistics, transportation, or operations\-focused software.
- Experience applying AI within governed enterprise environments, including FERPA\-relevant or similarly regulated data.
- Relevant AWS, security, SRE, or platform certifications.
What Success Looks Like
At 6 months: There is a working path to production that the pod uses to ship without hand\-built deployment each time. Automated quality, security, and policy gates catch common defects in agent\-generated code before they reach production. Baseline observability, rollback, and incident response are in place, and whoever is shipping through the path by then trusts it enough to use it routinely.
At 12 months: The path keeps pace with the pod's output, and both lead time to production and change\-failure rate have measurably improved. The delivery platform supports more than the original pod and is starting to set the pattern for how agent\-built software ships across IT. The golden paths, automated gates, and observability you established are reused by other teams.
Working Style
The role takes initiative and judgment. You will define a path where none exists, make progress with limited direction, raise risk early, and stay accountable for the quality and behavior of what ships.
Speed here depends on discipline. You will set clear gates, prove safety automatically, and keep ownership of production even when agents wrote the code moving through it.
First for a reason:
At First Student, we are a family of 60,000\+ employees who take pride in safely transporting more than 5 million students and passengers to and from their destinations each day! Our family of brands include Transco, Total Transportation, Maggies Paratransit, and GVC II. Our employees are at the forefront of safety and innovation; they create and implement the most advanced training and technology the transportation industry has to offer.
In the state of Washington, all technician and driving positions, including but not limited to van drivers and any other position requiring employees to drive a company\-owned vehicle, are considered safety\-sensitive and are therefore subject to drug and alcohol testing, including cannabis.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status. First is also committed to providing a drug\-free workplace. First will consider for employment qualified applicants with criminal histories consistent with the requirements of the San Francisco Fair Chance Ordinance, Los Angeles Fair Chance Ordinance, and any other fair chance law. Philadelphia's Fair Criminal Record Screening Standards Ordinance Poster is at this link or upon request https://www.phila.gov/media/20210423160847/Fair\-Chance\-Hiring\-law\-poster.pdf.
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 First Student, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
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.
First Student AI Hiring
First Student has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cincinnati, OH, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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.
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