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About This Role
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:
AutoStore is transforming how we operate, serve customers and partners, and scale our commercial organization.
We are looking for a Senior AI & Automation Engineer to build and deploy intelligent solutions that fundamentally improve how work gets done.
Your first proving ground will be Sales Support, a global operation with rich data, complex workflows and significant opportunities for AI and automation. From there, the capabilities you build will expand across the broader Commercial organization.
This is a hands-on role for someone who can move from business problem prototype production measurable impact.
You won't simply automate existing processes. You'll help us rethink them.
Key Tasks and Responsibilities:
You will design AI-powered capabilities combining agentic AI, automation, enterprise data and workflow orchestration, including:
- AI agents that understand requests, retrieve knowledge, reason across business rules and recommend or take actions
- Intelligent case intake, classification, routing and prioritization
- Automated order and PO quality validation
- Knowledge and support agents grounded in enterprise data and documentation
- AI-assisted exception handling and next-best actions
- Pattern recognition across operational data to identify recurring issues and opportunities
- Intelligent self-service experiences for employees, partners and customers
- The goal is simple: less manual work, better decisions, faster execution and better customer and partner experiences.
What You'll Own
- Build Agentic AI — Design, prototype and productionize AI agents and intelligent workflows using LLMs, RAG, tool use, orchestration and human-in-the-loop patterns.
- Find the Right Problems — Work directly with business teams and operational data to identify friction, repetitive work, rework and high-value opportunities. Challenge unnecessary complexity before automating it.
- Turn Data Into Intelligence — Use historical and real-time data to uncover patterns and create reusable intelligence for classification, recommendations, knowledge retrieval and decision support.
- Engineer for Production — Move beyond demos. Build secure, scalable and observable solutions with strong practices around APIs, integrations, testing, evaluation, monitoring and governance.
- Deliver Measurable Impact — Measure success through outcomes such as cycle time, quality, manual touches, self-service, adoption and capacity created.
- Scale Across Commercial — Take successful capabilities beyond Sales Support into sales productivity, partner and customer experience, revenue and account intelligence, and digital commercial experiences such as AutoStore Sphere.
Key Qualifications:
We care more about what you can build and how you think than matching every technology on a checklist.
- Strong software engineering skills, particularly Python and modern APIs
- Experience building applications using LLMs, generative AI or agentic AI
- Experience with RAG, enterprise search, tool/function calling or agent orchestration
- Strong understanding of data, workflow automation and systems integration
- Experience taking AI solutions from prototype to production
- Understanding of AI evaluation, observability, security and responsible AI
- Ability to turn ambiguous business problems into simple, scalable solutions
- Curiosity, ownership and a strong bias toward action
Experience with Azure, Databricks, Salesforce, SAP, GitHub or modern AI development frameworks is valuable, but the ability to learn, connect systems and ship matters more than any specific technology. *
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.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
autostore AI Hiring
autostore has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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