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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 building the operating system for the modern supply chain — a unified platform that powers order management, warehousing, transportation, and consumer experience for brands doing over $10B in commerce annually. Behind that platform is a large, fast\-moving organization where ops teams, finance, CX, and logistics are still doing work that should not require a human.
That is where you come in. As a Senior Forward Deployed Engineer on the AI Enablement team, your job is to get close to the people doing that work, understand exactly where the friction lives, and build agentic AI systems that make it disappear permanently. Not prototypes. Not internal demos. Production tools that change how Stord operates — with your fingerprints on the outcome.
This role is not a traditional product engineering position. You will operate more like an embedded problem\-solver than a member of a feature team — pairing engineering depth with the curiosity and judgment to identify what is actually worth building. If you have shipped internal AI tooling at scale, move fast without breaking things, and get energy from seeing the people around you work better because of what you built — this role was designed for you.
### Why This Role
- Your users are Stord employees — you get direct, immediate feedback on the impact of what you build
- Greenfield scope: we are early in systematically automating internal workflows with AI, and you will define how it's done
- High autonomy: observe a problem, propose a solution, ship it — with minimal bureaucratic drag
- Your work compounds: reusable modules and patterns you build accelerate every automation that follows
- AI investment is a company\-level mandate — you will have organizational support and visibility
### What You'll Build
### Tools for Internal Teams (CX, Finance, Ops, Logistics)
- AI\-powered tooling for Customer Experience teams — triaging issues, surfacing context, automating resolution workflows
- Finance automation — reconciliation, exception handling, and reporting workflows that currently require manual effort
- Ops and logistics tooling — dashboards, alerting, and intelligent interfaces that reduce the manual burden on warehouse and transportation teams
- LLM\-powered interfaces that let non\-technical teams query, act on, and get answers from operational data without needing engineering support
Workflow Automation
- End\-to\-end automation pipelines: observe workflow prototype validate with domain experts harden ship to production
- Agentic systems with proper logging, retries, monitoring, and edge case handling — not scripts that break silently
- Integrations with internal systems (OMS, WMS, TMS, Billing) via APIs and event streams
- Reusable automation modules that accelerate future workflow projects across the organization
Developer Productivity Tooling
- Internal AI\-powered tools that reduce friction across the engineering development lifecycle
- Lightweight APIs and integrations that connect the systems engineers rely on daily
- Tooling that helps engineers at Stord write, review, and ship code faster using agentic workflows
Internal Data and Analytics Products
- Automated reporting and alerting that replaces manual data pulling and analysis
- Dashboards and data products that surface operational intelligence to the teams who need it
- Self\-serve data interfaces that reduce the back\-and\-forth between operational teams and engineering
### How You Work
- Embed with a team, map the workflow end\-to\-end, identify the highest\-leverage automation target
- Ship small, validate with domain experts, iterate fast — production discipline from the very first commit
- Collaborate with Product Engineering when automations touch core platform systems; operate independently everywhere else
- Document and publish reusable patterns so the next engineer — or the next automation — goes faster
What We're Looking For
Required
- TypeScript / Node.js (3\+ years): Production backend experience. You reach for the right tool, and this is your primary one.
- Agentic AI development: You have built AI agents that automate real workflows in production — not toys, not demos.
- CLI\-native workflow: Claude Code, Cursor, Codex, or equivalent is your primary development environment. This is a hard requirement.
- LLM integration: Proven experience with OpenAI, Anthropic, or equivalent — tool use, structured outputs, prompt engineering, error handling.
- API design \& integration: You have built RESTful APIs from scratch and integrated with complex internal systems.
- Observability: You instrument your agents in production — logging, tracing, monitoring, alerting. You know when things break before users do.
- Database: Advanced SQL with PostgreSQL. You can model data and write queries that matter.
- High agency: You identify the problem worth solving, propose the approach, and drive to done with minimal direction.
- Production discipline: Fast iteration does not mean fragile systems. You build things that stay running.
Required Soft Skills
- Ownership \& Accountability — You own features end\-to\-end and take pride in what you ship. You follow through from design to production and don't drop things.
- Strong Communication — You can explain technical decisions and trade\-offs to engineers, PMs, and stakeholders. You ask good questions and listen well.
- Collaborative Approach — You work well with others, give constructive code review feedback, and actively seek input from teammates.
- Production Mindset — You prioritize reliability and user impact. You think about failure modes, monitoring, and operational concerns as part of your design process.
- Learning Agility — You're comfortable with rapidly evolving AI/ML technologies and tools. You stay current without chasing hype.
- Directed AI\-Assisted Development — You know how to use AI coding tools as a productivity multiplier while maintaining quality and your own technical judgment.
Strongly Preferred
- Experience automating workflows in operational or back\-office contexts (finance, support, logistics, HR)
- Familiarity with Stord's stack: Elixir/Phoenix, TypeScript, Kafka, GCP
- Vector databases and semantic search for internal knowledge retrieval
- Experience building internal developer tools or platforms
- Python for scripting, data wrangling, or model integration
Nice to Have
- Early\-stage startup background — you have worn many hats and shipped under pressure
- Hackathon experience or open source contributions
- Domain knowledge in logistics, supply chain, or operations\-heavy B2B environments
- Experience with workflow orchestration tools (Temporal, Prefect, Airflow, or similar)
What Success Looks Like
In your first 30 days, you have embedded with at least one internal team, mapped a workflow end\-to\-end, and shipped an automation — however small — into production. By 90 days, you have eliminated a meaningful chunk of manual work that was previously just accepted as the cost of doing business. By six months, you have a library of reusable patterns others are building on, and internal teams are coming to you with problems rather than waiting to be found.
About Stord
Stord is a cloud\-based supply chain platform that enables brands to compete and grow through end\-to\-end logistics solutions. We process over $10B in commerce annually and operate across Order Management (OMS), Warehouse Management (WMS), Transportation Management (TMS), Consumer Experience, and Demand Planning. We are backed by leading investors and are rapidly scaling our engineering organization to match our ambitions.
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. Mid-level AI roles across all categories have a median of $200,000.
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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