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About This Role
Insperity provides the most comprehensive suite of scalable HR solutions available in the marketplace with an optimal blend of premium HR service and technology. With more than 90 locations throughout the U.S., Insperity is currently making a difference for thousands of businesses and communities nationwide.
Behind our success is the unshakeable belief in the value of our people. We value diversity, inclusivity and a sense of belonging. We celebrate work and life events, and we partner with our clients and communities to make great things happen.
We’ve earned recognition time and again as a top place to work—named among the best by respected organizations like Glassdoor and U.S. News \& World Report. We’re also proud to be recognized for one of the country’s Top 50 Midsize Early Talent Programs through RippleMatch’s Campus Forward Awards. There’s never been a better time to be part of Insperity, and our best work is still ahead. Learn more at Insperity.com.
Why Insperity?
Flexibility: Over 80% of Insperity’s jobs have flexibility. We want your time to have balance, whether it’s spent with coworkers, clients, family or your community.
Career Growth: Insperity provides many ways to grow with the company. We offer continuous learning programs, mentorship opportunities and ongoing training.
Well\-Being: Our total rewards package includes generous paid time off, top\-tier medical, dental and vision benefits, health \& wellness support, paid volunteer hours and much more. We take care of our people so that you can do your best work.
We are seeking an Artificial Intelligence Engineer III to join our team
Summary:
This position is responsible for designing, delivering, and owning complex, production grade artificial intelligence systems that address ambiguous and high impact business problems. This role applies advanced software, data science, machine learning, and LLM engineering expertise to build AI powered applications, model driven solutions, and intelligently automated workflows. The AI Engineer III operates with a high degree of autonomy, making architectural and technical decisions while ensuring solutions are reliable, measurable, secure, and aligned with enterprise AI standards.
Responsibilities:
- Designs and owns end\-to\-end AI solutions, including LLM\-based applications, classical machine learning models, and hybrid approaches.
- Builds and operates complex AI systems such as agentic workflows, retrieval\-augmented generation (RAG) platforms, and decision\-support solutions.
- Evaluates ambiguous business problems and determines appropriate AI patterns, modeling techniques, and system architectures.
- Defines and implements evaluation strategies, metrics, and feedback loops to measure model effectiveness and business impact.
- Leads the development of data pipelines, feature strategies, and evaluation datasets required to support AI systems.
- Owns production readiness, including performance, reliability, cost, observability, and failure handling.
- Identifies and mitigates risks related to bias, hallucination, data leakage, and unintended AI behavior.
- Collaborates cross\-functionally with product management, platform teams, and stakeholders to guide solution design and delivery.
- Mentors and provides technical guidance to other team members.
- Contributes to the establishment and evolution of AI engineering standards, patterns, and best practices.
Qualifications:
- Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related technical field, or equivalent practical experience.
- Five or more years of professional experience spanning software engineering, data science, machine learning engineering, or AI system development.
- Advanced proficiency in software engineering, including Python, APIs, testing, and system design.
- Advanced proficiency in one or more large scale data platform such as Snowflake, Databricks, Amazon Redshift, Microsoft Synapse, or similar.
- Strong applied knowledge of machine learning and artificial intelligence concepts, evaluation techniques, and failure modes.
- Hands\-on experience designing, building, and deploying LLM based systems using enterprise grade platforms and frameworks.
- Experience with one or more AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar.
- Deep understanding of data quality, feature relevance, and model behavior across structured and unstructured data.
- Experience deploying, monitoring, and iterating on AI systems in production environments.
- Ability to make and defend technical tradeoff decisions balancing accuracy, cost, risk, and scalability.
- Strong communication and leadership skills, with the ability to influence technical direction and mentor others.
This job specification should not be construed to imply that these requirements are the exclusive standards of the position. Incumbent will follow any other instructions, and perform any other related duties, as may be required by the supervisor.
*Insperity provides a reasonable compensation range for each posted role in accordance with applicable pay‑transparency laws. Actual compensation is influenced by a variety of job‑related factors, including skills, experience, and geographic location.*
*The compensation range for this position, representing the pay span across all locations where this role may be filled is*
138,950 \- 169,100*At Insperity, we celebrate the diversity of our employees and our leadership. Insperity is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status or any other characteristic protected by law, including criminal arrest and/or conviction records.*
Salary Context
This $138K-$169K range is below the median for AI/ML Engineer roles in our dataset (median: $184K across 1486 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 2,799 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Insperity, 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 $175,000 based on 11,128 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $159,385. This role's midpoint ($154K) sits 12% below the category median. Disclosed range: $138K to $169K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $252,000. 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: $97,760; Mid: $159,385; Senior: $227,500; Director: $242,000; VP: $250,000.
Insperity AI Hiring
Insperity has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in TX, US. Compensation range: $169K - $169K.
Location Context
Across all AI roles, 16% (460 positions) offer remote work, while 2,318 require on-site attendance. Top AI hiring metros: New York (2,241 roles, $208,300 median); San Francisco (1,822 roles, $252,000 median); Los Angeles (1,611 roles, $188,900 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 2,799 open positions tracked in our dataset. By seniority: 98 entry-level, 1,283 mid-level, 1,092 senior, and 326 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (460 positions). The remaining 2,318 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 30 roles); AI Safety ($274,200 median, 43 roles); Research Engineer ($260,000 median, 387 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 2,799 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,978), AI Software Engineer (197), Data Scientist (195). 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 (98) are outnumbered by mid-level (1,283) and senior (1,092) 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 326 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (460 positions), with 2,318 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 $252,000, 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 (1,433 postings), Aws (840 postings), Rag (663 postings), Azure (639 postings), Gcp (537 postings), Pytorch (445 postings), Prompt Engineering (418 postings), Claude (396 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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