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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.
Director of Embodied AI, Data Foundry
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Stord operates the largest independent e\-commerce fulfillment network in the US — 20\+ fulfillment centers, 4,000\+ warehouse associates, and nearly 100 million packages shipped annually. We are building a new business line that turns this operational infrastructure into some of the most valuable training data assets in physical AI.
We are looking for an early leader to build this business from the ground up.
What You Will Own
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You are the first dedicated hire for this initiative, reporting directly to the CTO and Co\-Founder.
You will own the early Embodied AI business line: the product, the customers, the capture program, data operations, the team, and the P\&L. This is a builder\-operator role. You will:
- Close customers. You will run customer discovery and close initial pilot deals with humanoid robotics, labs, and physical AI companies. You will sit across from their VPs of AI and Heads of Data, understand what their training pipelines need, and translate that into what Stord delivers.
- Define and deliver the product. You will own the data product across quality tiers — from RGB egocentric video through depth\-enhanced and full multimodal capture with hand pose and annotations. You will decide what gets built, in what order, based on what buyers will actually pay for. You will hold the line on quality.
- Run the capture and delivery program. You will stand up the warehouse capture operation: camera and rig hardware selection, enrollment, edge processing, and the processing pipelines that package datasets for delivery. You will coordinate across warehouse operations, engineering, and customers to ship datasets on spec and on schedule.
- Build the team. You will hire and lead a small, incredibly senior team, expanding as the business scales. You set the technical direction and quality bar; they build. You make the architecture and tradeoff decisions; you do not need to write every line of code, but you need to be technical enough to know when the design is right.
What You Bring
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- You have run a data operation or data product end to end at a robotics or AI data company. You have owned the full lifecycle: defining what data to collect, managing how it gets captured and annotated, deploying hardware capture systems, ensuring quality, and delivering it to ML teams who depend on it.
- You are technical enough to lead engineers and make architecture decisions, but your core strength is execution across many moving parts. You can read a pipeline design and know if it is sound. You understand computer vision and ML data formats well enough to talk credibly with both your engineers and your customers. You do not need to be the one writing the pipeline — you need to be the one who makes sure the right pipeline gets built and the right data gets delivered.
- You have commercial instinct. Either you have sold data products to robotics or AI labs, or you have been the ML customer buying data and know exactly what separates a dataset worth paying for from one that is not. You can run a discovery conversation, translate customer needs into a product spec, and carry a deal to close.
- You operate across altitudes without friction — negotiating pilot terms in the morning, reviewing an annotation quality report at noon, and briefing a warehouse general manager on deployments in the afternoon. This role lives at the intersection of product, commercial, operations, and engineering, every day.
- You know the physical AI landscape. You understand who the humanoid robotics players are, what vision\-language\-action models need to train, and why egocentric demonstration data is the bottleneck for deploying robots into the real world.
Why This Role
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This is a rare opportunity to build a high\-growth business from the ground\-up with infrastructure and resources to support. You will have:
- A structural moat that no startup can replicate
- Direct access to the fastest\-growing buyer market in AI
- A CTO/Co\-Founder as your direct partner who will run initial buyer conversations alongside you and remove internal obstacles.
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. 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.
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.
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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