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About This Role
Your Benefits
SES offers eligible employees comprehensive benefits packaging including:
- Medical, Dental, Vision Insurance
- Free Life and AD&D Insurance
- 401K with 4% Company Match
- Paid Time Off -First Year!
- Paid Holidays
Position Summary Superior Environmental Solutions (SES) is a national industrial and environmental services company with about 950 employees across 30 dispatch branches in 17 states. Roughly 770 of them work in the field as technicians, drivers, warehouse associates, and frontline supervisors. SES is building the Field First Mobile App: a tool that lets a field team member ask a question by voice or text, about safety, equipment, procedures, compliance, HR, or IT, and get the answer a seasoned subject matter expert would give, or a clear path to the right person when the tool does not have an approved answer.
We are hiring an AI Product Manager to own this product end to end: discovery with field crews and department leaders, deciding what the first release answers, directing the content that makes the tool trustworthy, designing how it answers and when it escalates, recommending whether SES builds or buys, leading the pilot, measuring results, and scaling across branches. This person is hands-on. They write the tool's instructions, load and tag content, run the question bank, and read the logs. They do not develop the app; IT/IS, a contractor, or a vendor does that under their direction.
The role sits in Strategic Programs and works closely with the Chief Information Officer and the IT/IS team, who own and approve the technology stack, security, and system connections, and with content owners in HSE, Operations, Fleet, HR, and IT. This is a business-first product role, and success is measured in field outcomes: fewer routine calls to headquarters, time returned to supervisors and crews, faster onboarding, and support departments that absorb company growth without adding headcount. Once the Field First Mobile App is scaling, the role grows to additional AI products for operations and other departments. Essential duties and responsibilities* Lead field discovery: branch visits, ride-alongs, and interviews with supervisors, mechanics, and department leaders. Turn what you hear into a ranked list of question types with frequency, risk, difficulty, and first-release fit.
- Take over the pre-hire question inventory (the ten most asked questions from each department) and extend it into product requirements for the first release: topics, which experience launches first (the Field experience on SES tablets or the Administrative portal on any device), pilot branches, sign-in approach, and offline needs.
- Own the product roadmap. Present findings and recommendations to the steering committee and executive sponsors in plain, decision-ready terms.
- Design and own the content library: where approved content lives, how it is structured and tagged, and the governance process for updates, so every document has one named owner and one approved version.
- Work with content owners and subject matter experts to write down SES knowledge in a form the tool can use, including troubleshooting decision trees and who-to-call escalation trees. Write the first topics to set the pattern. Direct a contract technical writer as topic volume requires.
- Use AI to review SES's existing policies, procedures, and manuals before they are loaded. Find gaps, inconsistencies, and conflicts between documents and bring them to the content owner to resolve, so the tool answers from one correct version.
- Write and tune the tool's standing instructions: how it sounds, when it answers, when it says "stop, contact this person," and what it never discusses.
- Build and maintain a question bank of 100 to 200 field questions with known correct answers. Run accuracy and escalation testing before every release and hold the accuracy bar (proposed 90 percent). Review thumbs-down answers weekly and fix the content, tags, or instructions behind them.
- Partner with HSE to flag safety and compliance question types (answer automatically, answer with a caution, or always send to a person) and to check every safety and compliance answer before launch.
- Evaluate build versus buy for each experience. Score vendors, platforms, and AI services on fit for the field (voice, offline use, reading levels, sign-in), cost, and ownership, and bring a recommendation to leadership.
- Direct the build: write requirements and acceptance criteria, run user acceptance testing with pilot supervisors, and manage vendors and contractors to scope, budget, and schedule.
- Partner with the CIO and IT/IS on security, sign-in, hosting, data governance, and system connections. IT/IS approves the technology stack.
- Manage the program plan, budget, risks, and stage gates from discovery through pilot to scale. Report progress and results to sponsors monthly.
- Run the 90-day pilot at two branches with a weekly fix cycle. Prepare the readout and the scale decision.
- Lead adoption: co-design with pilot supervisors and Superior Leaders graduates, train branch champions, and build job aids that work for varied reading levels and gloved hands.
- Baseline and measure results: calls and tickets reaching headquarters, time returned to the field, onboarding speed, and avoided headcount growth in support departments.
- Scale in small steps: branches in waves, new topic areas, then system look-ups and actions one at a time, then Spanish. Prove each step before adding the next.
- After the Field First Mobile App is scaling, identify and lead additional AI products for operations and other departments using the same discovery, content, and testing method. Education and Experience
Required* Bachelor's degree in Business, Engineering, Information Systems, Computer Science, or a related field, or equivalent experience.
- 5+ years in product management, program management, or technology implementation, including at least one product or tool shipped to a few hundred non-technical users and carried through adoption.
- Hands-on experience building a working AI assistant or automation with a large language model: writing its instructions, organizing content so it can find the right passage, connecting it to at least one live system, and testing it with users.
- Demonstrated ability to spend time with frontline workers, earn their trust, and turn what they say into requirements.
- Experience leading cross-functional work with IT, vendors, and department leaders, including budgets, vendor evaluation, and stage-gate plans.
- Clear writer and presenter who can brief an executive team and a crew lead in the same week.
- Able to travel 25 to 40 percent.
Preferred* Experience in industrial services, construction, field service, utilities, fleet, or manufacturing with a frontline workforce.
- Experience with AI assistants that answer from a company's own documents (knowledge bases, search indexes, question banks for testing) and with escalation or guardrail design.
- Familiarity with Claude, Microsoft Copilot Studio, Amazon Bedrock, Salesforce Agentforce, or ready-made frontline platforms such as MangoApps, Beekeeper, or WorkJam.
- Familiarity with Salesforce, Microsoft 365, ADP, Zendesk, Samsara, or similar systems.
- Technical writing or instructional design experience with procedures and decision trees.
- Spanish proficiency. Key qualities for success* Field first. Chooses a day with a crew over a day in a conference room, and comes back with the ten questions that matter.
- Owner. Takes the product from idea to measured results and holds the accuracy bar under schedule pressure.
- Plain speaker. Explains what the tool does, and does not do, to a technician and to the CEO without jargon.
- Builder. Comfortable writing instructions, tagging content, running tests, and reading logs, not only planning the work.
- Careful with safety. Understands that the tool must say "stop and contact this person" rather than guess, and designs for that.
- Gets others to deliver. Keeps content owners, IT/IS, and vendors on schedule without having authority over them.
- Small steps. Proves one thing works in the field before adding the next. P**lease note this Job Description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. The position will also undertake other responsibilities and duties that may be assigned by SES from time to time.
S**ES is an Equal Opportunity Employer and complies with all federal, state, and local employment laws, including the Americans with Disabilities Act (ADA). Reasonable accommodations will be made for qualified individuals with disabilities to perform essential job functions.
S**ES provides equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, gender, national origin, age, disability, veteran status, sexual orientation, gender identity, or any other protected status under federal, state, or local laws.
This job description is not a contract of employment. Employment is at-will and may be terminated at any time with or without cause.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Superior Environmental Solutions, 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 $211,950 based on 13,048 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 $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Superior Environmental Solutions AI Hiring
Superior Environmental Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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