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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
Box has a world class security organization responsible for protecting our customer and employee data around the globe. We are looking for a Senior Enterprise AI Security Engineer to join our Enterprise Security team and own how Box adopts AI internally — safely, quickly, and at scale.
This role pairs enterprise security architecture with hands-on engineering. You will assess the AI tools and agent workflows Boxers want to use, then convert each assessment into a control that holds for everyone who adopts something similar later. You do not need to have trained a model, but you do need working intuition for the failure modes: an agent handed more authority than its task requires, untrusted text arriving as instructions, or company data leaving quietly inside an API call.
You will partner closely with IT, Infrastructure, Incident Response, Legal, Privacy, and the business functions piloting AI, shaping the technical direction of Box's secure AI adoption program.
WHAT YOU'LL DO
Core Mission
Design, build, deploy, and maintain comprehensive security systems that allow Box's workforce use AI tools and agents productively without losing visibility, identity boundaries, or control of company data.
- Architect and operate the control plane for workforce AI: AI gateways, prompt and response logging, and egress controls across approved assistants and copilots/agents
- Extend identity and access management to agentic and non-human identities — scoping, credential lifecycle, delegation, OAuth grant review, and revocation for agents and connectors reaching into corporate SaaS
- Gate what internal agents can reach: an approval path for MCP servers and connectors, scoped permissions per tool, and isolated execution for anything that runs code
- Harden the AI-era endpoint and browser layer: enterprise browser policy, extension governance, AI-assisted developer tooling, and local agent runtimes
- Build the shared enforcement layer — policy engines, brokers, approval registries, internal libraries — that catches risky behavior in flight: injected instructions, attempts to talk around a restriction, tools used outside their intended purpose, sensitive data heading somewhere it should not, and agents behaving nothing like they did last week
- Lead security architecture reviews and threat modeling for AI vendors, AI features enabled inside existing SaaS platforms, and internal agent deployments, tracing where untrusted content, company data, and privileged credentials meet in each one
- Own the standards, reference architectures, and paved paths that define what approved AI usage concretely means at Box, and build reusable patterns
- Partner with IT, Infrastructure, Incident Response, Engineering, Legal, and Privacy to mature AI governance, including AI-specific response playbooks
- Automate through scripting, infrastructure-as-code, and orchestration instead of manual review queues, and bring AI-assisted investigation tooling to the security team itself
- Help raise the technical bar across the team through design and code review, mentoring and guidance
Future Focus
This role is for a security engineer who can meet today's adoption requests while building for fleets of agents acting on behalf of employees, delegated authority across systems of record, and corporate data flowing through models. Your work will directly shape how Box secures its people, protects customer data, and holds its position as a trusted leader in intelligent content management.
WHO YOU ARE
Box is 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, while providing safe adoption patterns for the company.
You are a security professional with:
- Bachelor's or Master's degree in computer science, information security, or a related field, or equivalent practical experience
- 6+ years in enterprise or corporate security engineering, security platform engineering, application or product security, or a combination — with meaningful recent time spent on AI or agentic systems
- Hands-on experience securing real AI deployments — enterprise assistants, internal agents and the tool ecosystems around them, grounded search or RAG over company data, or AI coding tools inside a developer environment
- A working mental model of how AI systems get abused and paired with the ability to analyze AI workflows to determine guardrails.
- Strong identity and access management fundamentals
- Experience with security automation in Python, Go, or a similar language, and a track record of building tooling, libraries, or platform controls
- Fluency in the emerging problems of this space: shadow AI, agentic and non-human identity, SaaS-to-SaaS integration risk, browser security, and data exfiltration paths
- Exceptional communication skills, with the ability to explain AI risk and its business impact to practitioners
- Proven ability to work independently with autonomy, manage ambiguity, and recommend secure but risk-profiled decisions
Nice to have
- A track record of shipping internal security platforms
- Exposure to isolation and permissioning for autonomous workloads, or to AI red teaming and evaluation work
BENEFITS
Visit this webpage to check out all of our exciting healthcare benefits: https://join.collectivehealth.com/box
For all other benefits, please check out: Box Benefits + Perks
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!
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.
#LI-Hybrid
Salary Context
This $190K-$237K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $190K to $237K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Box AI Hiring
Box has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $161K - $237K.
Location Context
AI roles in Chicago pay a median of $194,700 across 390 tracked positions. That's 9% below 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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