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About This Role
Remote
Full Time
First Management
United States
About the job
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CoAdvantage is an HCM company providing payroll, ASO, and PEO services to 16,000 clients. We deliver payroll, benefits, HR compliance, time/PTO, and risk management solutions, and we are building a governed AI platform that will become a primary source of differentiation versus AI\-native competitors. The AI program runs three substrates (engineering knowledge graph, analytics feature store, customer knowledge store) and a multi\-agent harness.
Position Summary:
CoAdvantage is establishing a structured experimentation function to evaluate, measure, and operationalize AI investments under a productivity\-unit (PU) framework. The AI Experimentation and Feature Store Lead is responsible for designing pilots, measuring outcomes against pre\-declared thresholds, and producing the evidence that determines whether a tooling investment scales, iterates, or is retired.
The role is quantitative and operational. The person in this role works directly with function leaders (implementations, payroll operations, benefits, contact center, underwriting) to instrument their workflows, run controlled pilots, and report realized τ (the per\-resource productivity multiplier) against forecast.
This is a hands\-on role. The Experimentation Lead is also accountable for the analytics feature store as a production substrate and manages two Data Scientists who work across both substrates: the PU framework (baselines, causal estimators, τ measurement) and the feature store (feature definitions and production models).
Core responsibilities:
Measurement and baselines
The Experimentation Lead is responsible for defining the productivity unit (PU) for each in\-scope function, establishing baseline per\-resource capacity (c\_r), and maintaining the canonical record of these baselines. This includes the data pipelines, the definitional documentation, and the variance bounds.
Pilot design and execution
For each candidate AI investment, the Experimentation Lead designs the pilot: scope, sample, control, duration, success thresholds, and quality gates. Pilots are pre\-registered with the Head of AI before launch. The Experimentation Lead runs the pilot through execution, including the operational coordination with function leaders.
Outcome measurement
At pilot conclusion, the Experimentation Lead produces a written readout containing realized τ, realized c\_r, quality impact, and a recommendation against the pre\-declared thresholds. Readouts are reproducible from the underlying data.
Stage\-gate and portfolio reporting
The Experimentation Lead maintains the quarterly PU dashboard reported to the executive team and board: realized τ by function, N forecast versus actual, cumulative cost impact, and pilot pipeline status.
Cross\-functional coordination
The Experimentation Lead works with finance to translate operational measurements into the cost case, with function leaders to access data and operations, and with vendors when pilots involve third\-party tooling.
Analytics feature store ownership
The Experimentation Lead owns the analytics feature store as a production substrate: feature definitions, lineage, freshness, access patterns, and the contract with downstream model consumers. The Experimentation Lead works directly with the data team to validate feasibility of proposed features (source availability, refresh cadence, governance) and is accountable for the operational health of the feature store alongside the Staff MLOps Engineer who runs its infrastructure.
Management of the Data Science team
The Experimentation Lead manages two Data Scientists, both of whom work across the PU framework and the feature store. Day\-to\-day responsibilities include allocating workstream leadership across the two\-person bench (Aetna pricing lead, contact\-center baseline lead, etc.), prioritizing the combined PU and model backlog, reviewing methodological choices (identification strategy, validation, robustness), unblocking access to data, and signing off on both PU baselines and model promotion to production. The Experimentation Lead is the methodological reviewer of record for every PU baseline published and every model that lands in the feature store.
AI\-assisted code and hands\-on practice
The Experimentation Lead is hands\-on with code. The role is expected to use AI\-assisted coding tools (Claude Code, Copilot, or equivalent) as a default development surface for analysis pipelines, feature definitions, and pilot instrumentation. The role is not deck\-only: working code is the deliverable that backs every readout.
Required Qualifications:
- Seven or more years of experience in a quantitative role: data science, operations research, business analytics, or industrial engineering. At least two of those years in a player\-coach or team\-lead capacity.
- Demonstrated experience designing and running controlled experiments or A/B tests on operational processes, not only on digital products. Candidates who have only run web experimentation should be able to articulate how the methodology transfers to back\-office workflows.
- Working fluency with Python and SQL. Comfortable authoring production\-grade analysis code, feature definitions, and pipelines without an engineering intermediary.
- Hands\-on experience with AI\-assisted coding tools (Claude Code, Copilot, Cursor, or equivalent) as a daily driver, with code commits or repositories to demonstrate the practice.
- Direct exposure to a feature store (Feast, Databricks Feature Store, Tecton, Vertex Feature Store, or an internal equivalent) — both as a consumer and as an owner of feature definitions.
- Direct experience translating operational metrics into financial impact: cost per unit, fully\-loaded labor cost, payback period, NPV. Comfort sitting in a finance review meeting.
- Written communication skills sufficient to produce executive\-grade readouts.
Preferred Qualifications:
- Prior experience in a PEO, HR outsourcing, BPO, or other labor\-intensive services organization.
- Direct exposure to AI or automation deployments and to the gap between vendor claims and realized outcomes.
- Familiarity with causal inference techniques (difference\-in\-differences, synthetic control) for situations where randomized pilots are not feasible.
- Background in process improvement methodologies (Lean, Six Sigma) as a supporting framework, not as the primary lens.
What success looks like at 12 months:
By the end of year one, the Experimentation Lead is expected to have:
- Established PU definitions and validated c\_r baselines for at least four CoAdvantage functions.
- Run at least six pilots end\-to\-end, with written readouts and clear scale\-or\-retire recommendations.
- Stood up the quarterly PU dashboard with executive sign\-off on the methodology.
- Built a documented playbook for pilot design that another analyst can execute against.
- Identified at least two scaled deployments where realized τ tracked within twenty percent of forecast.
- Brought the analytics feature store to a defined v1 (feature catalog, freshness SLAs, lineage, access controls) with at least three production models served from it.
- Onboarded and developed the two Data Scientists into independent operators across both substrates — each capable of leading a workstream on either the PU framework side or the feature\-store side without methodological hand\-holding.
EEO
CoAdvantage is committed to providing equal employment opportunities to all employees and applicants without regard to race, color, religion, national origin, ancestry, citizenship status, age, sex (including pregnancy, childbirth, breast feeding and pregnancy\-related medical conditions), gender, gender identity or expression, sexual orientation, marital status, uniform service member and veteran status, disability, genetic information, or any other characteristic protected by applicable federal, state, or local laws and ordinances.
Benefits
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Health Insurance
Dental Insurance
Paid Time Off (PTO)
Paid Holidays
401(k) Matching
Vision Insurance
Life Insurance
Salary Context
This $80K-$90K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At CoAdvantage, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($85K) sits 52% below the category median. Disclosed range: $80K to $90K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
CoAdvantage AI Hiring
CoAdvantage has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span US, Bradenton, FL, US. Compensation range: $80K - $90K.
Location Context
AI roles in Austin pay a median of $218,800 across 493 tracked positions. That's 9% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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