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About This Role
Job Description
AutoStore™ holds a simple yet powerful vision: to store and move things for everyone, everywhere. Founded in Norway, we've grown into a global technology company. AutoStore uses advanced software to automate and orchestrate order fulfillment. Our goal is to ensure orders arrive faster than ever, with minimal environmental impact. That’s how we help brands exceed customer expectations.
We have more than 1600 systems in nearly 60 countries, and we grow continuously as a community of employees, partners, customers, suppliers, and connected technologies. Automation should make life easier, and by listening carefully to our community, we innovate to meet the industry’s most complex needs. With AutoStore™, brands gain speed, efficiency, and improved workplaces. And much more floor space.
AutoStore – moving things forward.
The Role:
The AI Engineer develops AI based solutions that increase the overall productivity of the HW engineering team. This includes, creating data models to facilitate the use of AI tools, creating AI agents and models that reduce the time to do HW designs, reviews, and testing. The HW focused AI Engineer reports to the VP of HW NPD and Innovation Engineering located in Atlanta, GA.
Key Responsibilities:
- Connect and structure hardware data sources (Jira, Excel, ERP, and design/test systems) so AI tools have clean, usable data to work from
- Across the Hardware organization, reduce design review preparation and execution from weeks to hours by automating material generation and consolidation
- Generate documentation, design review materials, and structured outputs (checklists, comparison tables, risk assessments) in consistent formats
- Produce summaries, forecasts, and burndown charts from historical project data
- Build agentic workflows that track hardware module status and automatically remind, summarize, and escalate across the development process
- Support engineers developing physical AI (embedding intelligence into distributed systems such as robots, cameras, grids, and ports)
Key Qualifications:
- 0–5 years of experience delivering data and AI solutions, or equivalent
- Ability to create data structures that facilitate the optimal use of AI tools
- Ability to connect existing tools (Jira, Excel, ERP systems, etc.) via APIs to push and pull data
- Ability to produce structured output generation \- design review materials in consistent formats (Word, PowerPoint, PDF)
- Familiar with design tools, processes, and techniques required to deliver hardware solutions on schedule
- Strong problem\-solving skills and the ability to work both independently and as part of a team
- Excellent communication skills across organizational levels
- Bachelor's or Master's degree in electrical engineering, computer engineering, computer science, systems engineering, or a related field
Preferred Skills
- Familiarity with RAG (Retrieval\-Augmented Generation) to pull from libraries of historical designs, specs, and schematics for grounded suggestions
- Familiarity evaluating and integrating commercial EDA platforms with embedded AI/multimodal capabilities (e.g., Siemens Fuse, Cadence Allegro X AI, Quilter) — tool selection and integration, not model building
- Exposure to generative/autonomous design tools and an understanding of where they assist and augment engineering throughput
- Familiarity with developing physical AI solutions \- embedding intelligence into distributed systems (robots, cameras, grids, ports)
- Familiar with project management concepts
We Offer:
AutoStore believes in taking care of employees and is dedicated to providing a supportive and rewarding work environment. Join us in our mission to store and move things for everyone, everywhere.
- Comprehensive Medical, Dental, and Vision plans
- Health Savings Account (HSA) with a company contribution
- Generous Paid Time Off including 12 holidays, paid exercise time, paid volunteer time, and paid parental leave plans for all new parents
- Retirement 401(k) plan with employer match and discretionary profit sharing contribution
- Educational assistance and professional development programs, including mentorship/coaching programs with external industry leaders
- Additional benefits include Group Life Insurance, Voluntary Additional Life Insurance, Disability Insurance, Employee Assistance programs, and more!
AutoStore is an Equal Opportunity Employer that does not discriminate on the basis of actual or perceived race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth, pregnancy\-related conditions, and lactation), gender identity or expression (including transgender status), sexual orientation, marital status, military service and veteran status, physical or mental disability, genetic information, or any other characteristic protected by applicable federal, state, or local laws and ordinances.
Recruitment Agencies
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*AutoStore does not accept agency resumes or assistance. Please do not forward resumes to our jobs alias or AutoStore employees. AutoStore is not responsible for any fees related to unsolicited resumes.*
### Struggling to find your next dream job?
We believe that following a defined and structured recruitment process will ensure that we have a fair and equal process for all candidates. We focus on finding the right talent and have designed our process to reduce biases and have a good candidate experience. For that reason, we are not accepting open applications, but encourage you to set up a job alert on our recruitment page to be notified when the next opportunity arises within your field of interest.
### Unsure about which path is the right one for you?
Try our brand new AutoStore Pathfinder! Here you will be able to take a short questionnaire and read about where in our organization your skillset and personality can be a fit
### About Us
AutoStore, founded 1996, is a warehouse robot technology company that invented and continues to pioneer cube storage automation, the densest order\-fulfillment solution in existence. Our focus is to marry software and hardware with human abilities to create the future of warehousing. We are a value driven organization, with our people being our most important asset. Lean, Bold, and Transparent manifest itself in everything we do, and are pushing borders to increase efficiency, safety and develop new products for our customers. AutoStore was born out of the great idea for storing goods like Rubik's Cube, instead of Dominos. We are now looking for talent that share our values and curiousity for new technology and development
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 autostore, 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.
autostore AI Hiring
autostore has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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