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About This Role
Description:
SPS Commerce is a leading provider of cloud-based supply chain management solutions, serving a global network of retail trading partners. We foster a collaborative and inclusive work environment where innovation and continuous improvement are highly valued. Join SPS Commerce and be part of a dynamic team that's transforming the global retail supply chain!
Position Summary:
As an AI & Automations Builder, you will build and apply the agents, automations, and systems solutions that power AI enablement across the full HR landscape, including Workday, ADP, and our Learning Tech Stack. This is a hands-on technical role embedded in HR, with a scope that spans every platform HR runs on today, as well as the HR processes that fall outside any single platform's ownership. You will own the design and delivery of these solutions end to end, from identifying the opportunity through building, testing, and documenting the result.
This role reports to the Director of HR Technology & Analytics and partners closely with HR Technology, HR Operations, Business HR, and HR Centers of Excellence (Talent Acquisition, Total Rewards, Enterprise Enablement). You'll also partner with IT Application Engineering on release management, integration design, and roadmap planning. IT retains ownership of security, integration infrastructure, and system health across all HR platforms; this role owns the automations, agents, and systems solutions built on top of and across them. You'll work as a peer with SPS's enterprise AI Engineering team on shared agent patterns and infrastructure, and contribute to a cross-functional community of AI Enablement Engineers & Builders.
Key Responsibilities:
- Design, build, and maintain AI agents and automated workflows across our HR technology landscape, including Workday, ADP, and our Learning Technology stack, using tools such as Workday Extend, n8n, and Claude Code
- Own the technical approach for cross-platform automations and agent workflows end to end, from scoping through build, testing, and documentation
- Identify and build solutions for HR processes that fall outside existing system boundaries, where no single platform currently serves as the process owner
- Partner across HR Technology, HR Operations, HR Business Partners, and HR Centers of Excellence to identify where agentic AI and automation can remove friction from HR operations
- Build integrations and data flows across Workday, ADP, our Learning Technology stack, and our broader enterprise technology ecosystem
- Support People Analytics and reporting by building automated pipelines that improve data quality, timeliness, and self-service reporting
- Audit HR processes across the employee lifecycle, across all platforms and the gaps between them, to find manual or repetitive work and engineer more scalable solutions
- Collaborate with IT's Application Engineers, Technical Product Owner, and Scrum Master on release management, integration design, and roadmap planning
- Partner with SPS's enterprise AI Engineering team, as a peer, to stay aligned on shared agent patterns, frameworks, and infrastructure relevant to HR use cases
- Participate in the AI Enablement community of practice, sharing reusable agent patterns, workflow components, and lessons learned
Document workflows and agent logic clearly so they can be supported, audited, and improved by both HR and IT after handoff or rotation
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Required Qualifications:
- 3–5 years of experience as a software, automation, or solutions engineer, with demonstrated experience building and deploying automated workflows or AI-powered agents in production
- Bachelor's degree in a relevant field, or equivalent combination of education and experience
- Proficiency in Python and SQL as daily tools for building and troubleshooting workflows
- Experience building automated workflows using tools such as Zapier, n8n, or similar platforms
- Experience with integration design and enterprise system configuration across multiple platforms (Workday and/or ADP experience is a plus)
- Comfort owning ambiguous, cross-platform problems, including processes without a predefined system boundary
Strong documentation habits and a collaborative approach to knowledge transfer
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Preferred Experience:
- Familiarity with Workday, ADP, and HR Learning Technology platforms (Degreed, Training Orchestra, Ethena, Donut)
- Familiarity with AI/LLM tooling and agent frameworks (Claude SDK, LangChain, MCP) and how to apply them to enterprise workflows
- Exposure to Workday-specific tools such as Workday Studio, EIBs, or Workday Extend
Experience with data pipelines or reporting tools that support people analytics (e.g., Workday Prism, Power BI, Snowflake, Databricks)
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Location:
This role is hybrid out of our headquarters in Minneapolis, MN or open to fully remote candidates in the US or Canada. We Succeed Together through in-person collaboration, balanced with remote work to provide flexibility.
What We Offer:
At SPS Commerce, we are committed to ensuring that each employee's compensation reflects their unique experiences, performance, and skills in their role. The salary range for this role considers several factors, including education, relevant skills, work history, certifications, location, and more.
The annual salary range for this role is $85,000 – $120,000 USD OR $95,000 – $130,000 CAD + Equity. Final compensation will be determined based on the factors outlined above and may fall anywhere within the applicable range.
SPS Commerce offers a comprehensive benefits package designed to support employees' health, well-being, and financial security. Benefits are country-specific and aligned with local laws and market practices.
Commitment to our Employees:
At SPS we power connections that drive the world of commerce forward, and our success depends on making strong decisions, fostering innovation, delivering unparalleled customer solutions, and driving outstanding business performance. We achieve this by creating an environment where every employee feels a true sense of belonging. We embrace diversity, equity, and inclusion, ensuring everyone feels accepted, valued, and empowered to make a meaningful impact.
We are committed to affirmative action and equal opportunity in all aspects of employment. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Commitment to our Employees:
At SPS we power connections that drive the world of commerce forward, and our success depends on making strong decisions, fostering innovation, delivering unparalleled customer solutions, and driving outstanding business performance. We achieve this by creating an environment where every employee feels a true sense of belonging. We embrace diversity, equity, and inclusion, ensuring everyone feels accepted, valued, and empowered to make a meaningful impact.
We are committed to affirmative action and equal opportunity in all aspects of employment. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Salary Context
This $85K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At SPS Commerce, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($107K) sits 49% below the category median. Disclosed range: $85K to $130K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
SPS Commerce AI Hiring
SPS Commerce has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Minneapolis, MN, US. Compensation range: $130K - $130K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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