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About This Role
Stord is The Consumer Experience Company, powering seamless checkout through delivery for today's leading brands. Stord is rapidly growing and is on track to double our revenue in the next 18 months. To meet and exceed this target, Stord is strategically scaling teams across the entire company, and seeking energetic experts to help us achieve our mission.
By combining comprehensive commerce\-enablement technology with high\-volume fulfillment services, Stord provides brands a platform to compete with retail giants. Stord manages over $10 billion of commerce annually through its fulfillment, warehousing, transportation, and operator\-built software suite including OMS, Pre\- and Post\-Purchase, and WMS platforms. Stord is leveling the playing field for all brands to deliver the best consumer experience at scale.
With Stord, brands can increase cart conversion, improve unit economics, and drive sustained customer loyalty. Stord’s end\-to\-end commerce solutions combine best\-in\-class omnichannel fulfillment and shipping with leading technology to ensure fast shipping, reliable delivery promises, easy access to more channels, and improved margins on every order.
Hundreds of leading DTC and B2B companies like AG1, True Classic, Native, Seed Health, quip, goodr, Sundays for Dogs, and more trust Stord to deliver industry\-leading consumer experiences on every order. Stord is headquartered in Atlanta with facilities across the United States, Canada, and Europe. Stord is backed by top\-tier investors including Kleiner Perkins, Franklin Templeton, Founders Fund, Strike Capital, Baillie Gifford, and Salesforce Ventures.
Stord is the unified operating system for modern commerce. Without Stord, brands over\-spend on a fragmented web of point solutions with no shared data or execution layer. Stord's vertically integrated, AI\-powered platform of front\-end commerce software through back\-end fulfillment execution, gives brands everything they need before and after checkout. Inside our own walls, that shows up in how our teams work: using AI to build, develop, analyze, and optimize our business.
We are looking for someone to amplify that mentality across our whole business: Sales, Finance, People, Legal, Support, Operations, Marketing, Engineering, and IT and Security, building the processes, team, and single integrated back\-end platform that powers Stord’s teams and business functions.
This is a build role, not a research seat and not a reactive IT job. This individual is expected to own and drive internal business efficiency. You’ll execute with real ownership of Stord's enterprise systems and security, and the license to use AI as your primary tool for building better and faster than traditional business operations. You'll solve for Stord's own operations exactly how Stord solves for the brands we serve: one AI\-powered surface to run the business on.
You'll report directly to the SVP of Product \& Engineering and work closely with leadership across Finance, Revenue, People, Legal, and Operations. You'll have your own engineering and product resources, alongside IT, Security, and Internal Systems teams.
What You'll Own
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Company\-Wide AI Adoption
- Own Stord's AI adoption strategy across every internal team, spanning Sales, Customer Success, Finance, People, Legal, IT, and Operations
- Take the AI wins already happening across Stord and turn them into a shared standard: one AI\-native operating layer that every team builds on daily, not just talks about
- Own the security and compliance review process for AI tools adopted anywhere in the company, and own IT administration for AI platforms company\-wide (procurement, provisioning, access management)
- Partner with Stord's Product and Data leadership so your adoption work compounds what's already being built into our products, rather than duplicating it
- Report progress and ROI on AI adoption to executive leadership on a regular cadence
Forward\-Deployed Engineering \& Product
- Build and lead a forward\-deployed engineering and product team: engineers and PMs who sit inside Finance, People, Legal, Support, and Operations, find the real workflow problems, and ship working software and agents against them in weeks, not quarters
- This team builds, it doesn't just advise: default to shipping custom, AI\-native tooling when an off\-the\-shelf point solution can't move fast enough or doesn't fit how Stord actually works
- Run it the way a real product organization runs: genuine discovery, genuine requirements, a real shipping cadence, with success measured in adopted, working tools, not audits or frameworks
Building Stord's Operating Backbone
- Own the systems Stord runs on (CRM, ERP, HRIS, Procurement, Customer Success platforms, and more) and build them to keep pace with how fast we're growing
- Find the processes creating the most drag across Finance, People, Legal, Support, and Operations, and use AI to rebuild them, not just automate the old way of doing things
- Make deliberate build, buy, or cut calls on our technology investments as we scale, rather than letting the stack grow by accretion
- Set the roadmap and make the integration and architecture calls that let Stord's back office move at the same speed as the rest of the company
M\&A Integration
- Own the business systems integration whenever Stord acquires a company: get the acquired business running on Stord's enterprise stack and standards quickly, not left as a permanent side system
- Partner with Corporate Development and executive leadership on the technical and operational due diligence for future acquisitions
Data You Can Build On (in partnership with the VP of Data)
- Own governance for the data domains that matter most: customers, vendors, products, locations, employees, working closely with the VP of Data wherever this touches our broader data platform and analytics layer
- Set the standards that make our data a real asset \- clean enough that every team builds on it with confidence, no shadow spreadsheets required
- Partner with Finance, Revenue, and Operations to align on one shared definition of the metrics that matter
IT \& Security, Built for Speed
- Own corporate IT strategy and day\-to\-day operations: identity and access management, device lifecycle, network infrastructure, end\-user support
- Own security operations, built to move as fast as the rest of Stord without cutting corners on compliance
- Build processes that scale cleanly as we add headcount and expand geographically
Team Leadership
- Lead and grow a team across AI Enablement, Forward\-Deployed Engineering \& Product, IT, Security, and Enterprise Systems, with dedicated director\-level owners for each discipline so every function has a leader who can go deep
- Build the kind of accountability and rigor that makes people want to work with you
- Earn trust across functions outside your own reporting line; this role only works if people are willing to change how they work because you asked them to
What Success Looks Like
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In your first year:
- Stord's internal functions are running off a single AI\-powered operating system, with real numbers to show for it
- Your forward\-deployed engineering and product team has shipped and driven real adoption of custom tools or agents in at least two functions outside of engineering
- Every function at Stord has a real AI adoption story, active usage, and a measured impact
- If Stord has closed an acquisition this year, its business systems are already running on Stord's operating backbone, not sitting as a bolted\-on side system
- Stord has a clear, documented enterprise systems architecture with a prioritized roadmap
- Every team builds on data they trust, without needing a shadow spreadsheet to double check it
- IT is ahead of problems instead of reacting to tickets
- Security and compliance are tight around the systems that matter most, and ahead of both Stord's growth and external threats
- You've built a leadership team you trust, including dedicated Directors of IT and Security, known for getting things done
What We're Looking For
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Experience
- Has built and led a forward\-deployed or embedded engineering team: engineers who sit inside a business function, find the real problem, and ship working software fast, not a team that writes specs for someone else to build
- Has personally led an internal platform, tools, or applied AI/agents team, comfortable evaluating engineering work and making technical hiring calls, not just approving business requirements
- Has integrated systems, data, and teams following an acquisition, and can point to what actually got merged into one operating model, not left running in parallel
- 10\+ years in a senior technical or operating leadership role in a high\-growth business, with direct budget and hiring ownership
- Background in technology or tech\-enabled businesses; logistics or operations experience is a plus, not a requirement
Technical Depth
- Enough hands\-on technical background to earn real credibility with your own engineers: you can read a design doc, push back on an approach, and tell the difference between a genuine agentic workflow and a thin wrapper around an API call
- Deep fluency in enterprise application architecture, system integration, and data management (CRM \+ ERP \+ HRIS minimum)
- Working knowledge of the current AI tooling and agentic landscape, sharp enough to make build\-vs\-buy calls yourself, not just sign off on a vendor's security review
- Solid grounding in IT operations, identity management, and security controls \- you don't have to be the one doing the work, but you need to know when it's being done well
Leadership
- Earns trust across functions; Finance, Engineering, Revenue, and Operations all want to work with you
- Has led product and engineering talent specifically, and knows how to keep a build\-focused team shipping instead of drowning in stakeholder requests
- Real change management chops: you can get a Sales or Support team to actually adopt a new way of working, not just tolerate a mandate
- Comfortable being the person who makes a newly acquired company actually feel like Stord within months, not years
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 STORD Warehouse, 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.
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.
STORD Warehouse AI Hiring
STORD Warehouse has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Atlanta, GA, US, Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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