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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 operates one of the largest independent e\-commerce fulfillment networks in the U.S., with 20\+ fulfillment centers, 4,000\+ warehouse associates, and nearly 100 million packages shipped annually. We’re now building a new business line that transforms this real\-world operational infrastructure into high\-value training data for the next generation of physical AI.
We’re looking for an early leader to help build this business from the ground up—shaping the strategy, defining the operating model, and turning Stord’s physical network into a differentiated data asset for the future of AI.
Why This Role:
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This is a rare opportunity to build a new AI business from zero, backed by the physical infrastructure, operational footprint, and resources to give it a meaningful head start.
You will have:
- A structural data advantage. Direct access to one of the largest independent e\-commerce fulfillment networks in the U.S.—an operational environment that would be extremely difficult for a startup to replicate.
- Direct access to a rapidly growing buyer market. Work directly with the robotics companies, AI labs, and physical AI teams building the next generation of intelligent machines.
- A direct line to the founders. Partner closely with the CTO and Co\-Founder, who will join initial customer conversations, help shape the business, and remove internal obstacles.
- True ownership. You won’t be inheriting an established business. You’ll define the strategy, win the first customers, build the product and operation, hire the team, and ultimately own the P\&L.
This is an opportunity to build the company that turns real\-world operations into the data infrastructure for embodied AI.
What You'll Do:
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You will be the first dedicated hire for this initiative, reporting directly to the CTO and Co\-Founder. You will have end\-to\-end ownership of the early Embodied AI business—including the product, customers, data capture program, data operations, team, and P\&L.
This is a true builder\-operator role . You will:
- Win the first customers. Lead customer discovery and close initial pilots with humanoid robotics companies, AI labs, and physical AI companies. You’ll engage directly with VPs of AI, Heads of Data, and technical leaders to understand their training requirements and translate those needs into products Stord can deliver.
- Define and build the product. Own the data product roadmap across quality tiers—from RGB egocentric video to depth\-enhanced and full multimodal datasets with hand pose, annotations, and other metadata. You’ll prioritize what gets built based on customer demand and willingness to pay, while setting and maintaining a high bar for data quality.
- Build and run the capture operation. Stand up the warehouse\-based data capture program, including camera and rig selection, participant enrollment, edge processing, data pipelines, and dataset packaging. You’ll coordinate across warehouse operations, engineering, and customers to deliver datasets on spec and on schedule.
- Build the team. Hire and lead a small, highly senior team, scaling it as the business grows. You’ll set the technical direction and quality bar, make architecture and tradeoff decisions, and ensure the team builds the right systems. You don’t need to write every line of code—you need to know what good looks like and make sure it gets built.
Basic Qualifications:
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- End\-to\-end data operations experience. You’ve built or operated a data operation or data product within robotics, AI, or a related technical environment. You understand the full lifecycle—from defining what data to collect and deploying capture hardware, through annotation and quality control, to delivering production\-ready datasets to ML teams.
- Technical depth with strong execution. You’re technical enough to lead engineers and make sound architecture decisions, but your greatest strength is getting complex initiatives across the finish line. You can evaluate a pipeline design, understand computer vision and ML data formats, and communicate credibly with both engineers and customers. You don’t need to be the person writing the pipeline—you need to ensure the right pipeline gets built.
- Commercial instinct. You’ve either sold data products to robotics or AI companies, or you’ve been the ML customer buying data and understand what makes a dataset genuinely valuable. You can lead discovery, translate customer requirements into a product specification, and carry an opportunity from first conversation through pilot and close.
- Exceptional operating range. You’re comfortable moving between strategy and execution—negotiating pilot terms in the morning, reviewing an annotation quality report at noon, and briefing a warehouse GM on a deployment that afternoon. You thrive at the intersection of product, commercial, operations, and engineering.
- Deep understanding of physical AI. You understand the physical AI landscape, the needs of humanoid robotics companies and AI labs, and the role that high\-quality real\-world data plays in training vision\-language\-action models and deploying robots in production. You understand why egocentric demonstration data is one of the critical bottlenecks to scaling embodied intelligence.
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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
STORD Warehouse AI Hiring
STORD Warehouse has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Atlanta, GA, US, Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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