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About This Role
Job description
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We are building the next generation of how customers, clients, and colleagues interact with Safelite. Agentic AI is at the center of it.
The Product Director, Agentic Experiences is the single\-threaded owner of our agentic experiences across customer, client, and colleague audiences. You will turn vision and strategy into an executable roadmap, then run the build, the experimentation, and the performance that make these experiences real.
This is the senior operator who makes the agents ship and improve. You run discovery and delivery day to day, you are deep in the tools and the data, and you lead a team of product managers who each own a space. You bring structure to ambiguous work and keep a portfolio of agentic experiences moving across audiences and channels.
Own the agentic roadmap across customer, client, and colleague
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- Translate vision and strategy into an executable roadmap and clear priorities across all three audiences.
- Run discovery and delivery day to day, from problem definition through launch and continuous improvement.
- Make tradeoff decisions based on customer impact, business value, effort, risk, and learning potential.
- Balance near\-term performance, foundational platform work, and the next generation of agentic capability.
Build and improve agents as living systems
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- Scope, build, and ship agents that handle thousands of customer, client, and colleague conversations, and improve them in production rather than shipping and leaving them.
- Run the experimentation engine: deploy tests, simulate responses and outcomes, predict behavior, and find opportunities for agents to self\-improve.
- Own how agent performance is measured, from accuracy and resolution to revenue and customer experience, and act on what the data shows.
- Hold an accurate view of current capability at all times, and represent it accurately in every forum.
Lead the build with partners and engineering
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- Manage the platform partners who build alongside us, with clear standards for delivery and a tight working rhythm.
- Partner closely with Technology, Design, Analytics, Operations, Contact Center, Sales, and Client teams to deliver measurable outcomes.
- Bring clarity to ambiguous problems, frame the decisions that matter, and keep teams moving when the path is still being defined.
Lead and grow the product team
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- Lead, hire, and mentor a team of product managers, each owning a space within the agentic portfolio.
- Set the standard for AI fluency on the team, measured by what people build and learn, not by titles or credentials.
- Establish the ways of working that let a team operate on the front edge of an emerging space and still ship real, tangible product.
- 8\+ years of product management or digital product experience, with meaningful time owning products from strategy through launch and continuous improvement, and experience leading product managers.
- Experience shipping conversational, AI\-enabled, or agentic product in production, with real users and measurable outcomes.
- Demonstrated fluency using AI across the product lifecycle: strategy development, discovery, customer signal, analysis, and prototyping. Fluent with tools like Claude, Claude Code, Claude Design, ChatGPT, Cursor, Lovable, and Granola.
- Fluency building and improving agents in production: designing evaluations, simulating and testing agent behavior, and using observability to find where agents fail and improve them.
- Hands\-on use of digital analytics and performance platforms (Quantum Metric, Snowflake, or similar) to read funnel performance, conversion, appointment rate, and retention. You run your own analysis and form your own point of view from the data.
- Hands\-on fluency with the platforms agentic experiences are built on: agent platforms and LLM orchestration, eval and observability tooling, retrieval and tool\-use patterns, and voice and multimodal stacks. You make architecture and platform tradeoffs in partnership with engineering.
- Working understanding of agent integration architecture: APIs, webhooks, identity, consent, and how agents transact with other agents and systems.
- Strong written and verbal communication. Ability to operate in ambiguity and bring structure to complex problems.
We are looking for a product leader who has built intelligent, conversational, or agentic experiences in production, and who has done that work inside a fast\-moving, product\-led organization. The shape of the work matters more than the company name on the resume.
- Conversational and agentic product. You have shipped chatbots, virtual agents, or AI\-driven experiences, and you have lived through what it takes to make them reliable when they hit friction.
- Product environments where you have seen real practice work. Dedicated cross\-functional teams, real discovery, instrumented experimentation, and outcome\-based goals. We are early in standing up a modern product practice at Safelite, so you should be up for helping us build it here, and have done some of that work before.
- Pace\-setting product cultures. You have worked inside organizations that set the standard for how product gets built. Fast cycles, tight feedback loops, and a default to shipping and learning.
Strong fits include conversational AI and customer engagement platforms, companies that built early chatbot or virtual agent products and have moved into agentic systems, applied AI teams inside operating businesses, and product organizations known for shipping at pace.
- Experience leading both customer\-facing and internal associate\-facing AI experiences.
- Understanding of how agents transact with other agents and systems, and where interoperability is heading.
- Familiarity with modern experimentation practices for AI systems, including offline evals, online testing, and human\-in\-the\-loop review.
- Experience managing platform partners or vendors who build alongside an internal team.
- Roadmap Ownership. Turns vision and strategy into an executable roadmap and clear priorities across audiences.
- Builder Fluency. Builds with AI tools directly, understands the technology well enough to co\-create on it, and guides a team to do the same.
- Experimentation and Learning. Treats agents as systems to observe, test, simulate, and improve continuously.
- Business Acumen. Connects product decisions to revenue, conversion, satisfaction, and operational performance.
- People Leadership. Hires, leads, and mentors a team of product managers and sets the bar for how the team works.
- Cross\-Functional Leadership. Aligns teams, influences stakeholders, and drives accountability without relying on authority.
- Execution Discipline. Moves from strategy to delivery while managing scope, tradeoffs, dependencies, and outcomes.
This job description in no way states or implies that these are the only duties to be performed by an employee occupying this position. Employees may be required to perform other related duties as assigned to ensure workload coverage. This job description does NOT constitute an employment agreement between the employer and employee and is subject to change by the employer as the organizational needs and requirements of the job change.
This position description is not all inclusive for every aspect of this role. Reasonable accommodations will be made for individuals covered by ADA, ADEA, FMLA and other laws and regulations in accordance with their requirements. Physical and mental demands are not, and should not be construed to be job qualification standards, but are illustrated to help the employer, employee and/or applicant identify tasks where reasonable accommodations may need to be made when an otherwise qualified person is unable to perform the job’s essential duties because of an ADA disability.
Other qualifications may be required to ensure employment eligibility in accordance with local laws, regulations and with Safelite Group, Inc. policies and practices.
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Internal Associates: Already a member of the Safelite team? Apply through your Workday account by searching "Find Open Jobs".
Diversity: Safelite welcomes everyone. We value our diverse workforce and suppliers, and we’re proud to be an equal opportunity employer. Learn more at Careers http://safelite.com/Careers
*Benefit amounts are estimates only. Actual values will depend on benefit elections during enrollment.*
This position description is not all inclusive for every aspect of this role. Reasonable accommodation will be made for individuals covered by ADA, ADEA, FMLA and other laws and regulations in accordance with their requirements. Physical and mental demands are not and should not be construed to be job qualification standards, but are illustrated to help the employer, employee and/or applicant identify tasks where reasonable accommodations may need to be made when an otherwise qualified person is unable to perform the job’s essential duties because of an ADA disability.
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 Safelite, 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. Director-level AI roles across all categories have a median of $272,150.
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.
Safelite AI Hiring
Safelite has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Columbus, OH, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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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