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About This Role
WHAT IS BOX?
Box (NYSE:BOX) is the leader in Intelligent Content Management. Our platform enables organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, and transform business workflows with enterprise AI. We help companies thrive in the new AI\-first era of business. Founded in 2005, Box simplifies work for leading global organizations, including JLL, Morgan Stanley, and Nationwide. Box is headquartered in Redwood City, CA, with offices across the United States, Europe, and Asia.
By joining Box, you will have the unique opportunity to continue driving our platform forward. Content powers how we work. It's the billions of files and information flowing across teams, departments, and key business processes every single day: contracts, invoices, employee records, financials, product specs, marketing assets, and more. Our mission is to bring intelligence to the world of content management and empower our customers to completely transform workflows across their organizations. With the combination of AI and enterprise content, the opportunity has never been greater to transform how the world works together and at Box you will be on the front lines of this massive shift.
WHY BOX NEEDS YOU
You will work on problems that matter to the company, with executive air cover to actually change how things run. You will get to put modern AI into production behind real workflows, not demos. And you will do it inside an IT organization that treats engineering excellence, security, and business outcomes as the same job — not competing ones.
We are looking for an AI Business Automation Engineer to embed directly with functional Subject Matter Experts across Box — Finance, Legal, People, GTM, Customer Success, and beyond — to identify business processes ripe for reinvention and rebuild them with an AI\-first mindset. You will sit shoulder\-to\-shoulder with the people doing the work, deeply understand their workflows, and then design, prototype, and deliver the technical solutions that transform how those teams operate.
This role lives inside IT and carries the full weight of our infrastructure, data, integration, automation, and information security standards. The work spans the stack: one week you may be wiring an AI agent into a Finance close process, the next you may be redesigning a Legal intake workflow as an agentic system, and the next standing up a secure data pipeline so a GTM team can automate customer intelligence workflows. If you are energized by the intersection of business problems, modern AI, and pragmatic engineering — and you want your code in production, not in slideware — this is the role.
WHAT YOU'LL DO
- Partner directly with functional SMEs to map current\-state processes, identify friction and waste, and co\-design AI\-first reinventions that materially change how the work gets done.
- Translate business requirements into clear technical specifications, architecture diagrams, and delivery plans, then build the agentic solution end\-to\-end alongside teammates and senior engineers.
- Prototype rapidly using Python and modern AI tooling (LLM APIs, RAG patterns, agent frameworks, workflow orchestration) to validate ideas before scaling them into production.
- Build and maintain integrations between Box's core SaaS systems (Box, Workday, Salesforce, NetSuite, Atlassian, BigQuery, etc.) using APIs, iPaaS platforms, and custom services.
- Design and implement automations that retire manual work, replace brittle scripts, and unlock capacity for the business.
- Own the data plumbing — pipelines, models, access patterns — that makes AI solutions trustworthy and operable.
- Apply security, privacy, and compliance controls from day one: data classification, least\-privilege access, secrets management, auditability, and responsible AI guardrails.
- Operate what you build: monitoring, runbooks, incident response, and continuous improvement based on real usage.
- Evangelize what's possible. Bring SMEs along the journey so they become co\-owners of the AI\-first future, not passive recipients of it.
WHO YOU ARE
We are an AI\-first company. This means you approach your work with a growth mindset and find ways to leverage AI to help make faster, smarter decisions that will 10X your impact at Box.
- 2\-3\+ years of hands\-on engineering experience in IT or software engineering roles, with a track record of shipping production code that real users depend on.
- Deep experience with agentic coding platforms like Claude Code, Cursor, and Codex, as well as experience building custom agents that leverage MCP servers and CLIs
- Working proficiency in Python (or equivalent in Go, TypeScript, Java) — you write clean, tested, maintainable code and are comfortable contributing to a real codebase, not just notebooks.
- Demonstrated ability to translate business problems into technical specs and delivered software. You can write a clear design doc, build the thing, and explain it to a non\-technical partner.
- Working knowledge across several of the following, with depth in at least one: cloud infrastructure (GCP preferred), data engineering (SQL, warehouses, pipelines), integration patterns (REST, GraphQL, webhooks, event\-driven, iPaaS like Workato/SnapLogic), automation tooling, and IAM.
- Familiarity with information security fundamentals — authn/authz, encryption, secrets handling, and safe data practices — and a willingness to learn what it takes to build systems that pass an audit.
- Hands\-on experience applying modern AI to real problems: LLM APIs, prompt engineering, retrieval\-augmented generation, or building at least one AI\-driven workflow, prototype, or agent.
- Experience with agent frameworks (LangGraph, OpenAI Agent SDK, Claude Agent SDK, etc.), MCP, or building tool\-using systems at scale.
- Strong product instincts and a bias for shipping. You know when to deliver a thin slice and learn versus when to invest in something more durable.
- Excellent communication skills. You build trust with non\-technical partners and write specs, docs, and updates that travel without you in the room.
Box lives its values, with community and in\-person collaboration being a core part of our culture. Boxers are expected to work from their assigned office a minimum of 3 days per week.Your Recruiter will share more about how we work and company culture during the hiring process.
At Box, we believe unique and diverse experiences benefit our culture, our products, our customers, our company, and our world. We aim to recruit a passionate, high\-performing workforce that reflects the world we live in.If you are head\-over\-heels about this role but unsure if you meet all the requirements, we encourage you to apply!
EQUAL OPPORTUNITY
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability, and any other protected ground of discrimination under applicable human rights legislation. Box strives to respect the dignity and independence of people with disabilities and is committed to giving them the same opportunity to succeed as all other employees. Inclusiveness is core to our culture at Box, and we strive to ensure you get the most from your interview experience.
Box makes reasonable accommodations for applicants with disabilities. If a reasonable accommodation is needed to participate in the job application or interview process, please complete this form. Reasonable accommodations may include scheduling adjustments, document dictation and beyond.
Notice to applicants in Los Angeles: Box, Inc and its related branches will consider for employment, qualified applicants with criminal histories in a manner consistent with the Los Angeles Fair Chair Ordinance. The Fair Chance Ordinance is provided here.
Notice to applicants in San Francisco: Box, Inc and its related branches will consider for employment, qualified applicants with criminal histories in a manner consistent with the San Francisco Fair Chair Ordinance. The Fair Chance Ordinance is provided here.
For details on how we protect your information when you apply, please see our Personnel Privacy Notice. If you are a California\-resident, please read our California Applicant \& Candidate Privacy Notice here.
Salary Context
This $146K-$183K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 Box, 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. This role's midpoint ($164K) sits 23% below the category median. Disclosed range: $146K to $183K.
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.
Box AI Hiring
Box has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $183K - $183K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national 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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