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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 is looking for the next member of a highly impactful and visible team. This role will partner with our world\-class sales team and serve as the primary technical advisor for customers who want to build the next generation of enterprise applications and AI\-powered solutions using the Box platform. This role requires a strong technical background, project management experience, and the ability to present both technical and business materials. It also requires a willingness to learn, as you will rarely be
called upon to architect the same application twice. You will help define and accelerate innovative AI use cases on the Box platform by combining technical depth, product thinking, and customer insight. This is not your typical Solutions Engineer role. As a member of a small and well\-regarded team, you will be closely connected to product direction and have meaningful impact engaging with functional leaders across the business. You will be exposed to many different application environments, development patterns, enterprise architectures, and emerging AI workflows. The fact that no customer environment or development project is the same makes this a great role for learning new technologies and growing your skills and knowledge. There is always something new, and it is one of the many reasons people love this team.
WHAT YOU'LL DO
Sell: This is, after all, a Solutions Engineer role at the core. You'll be a key member of the technical opportunity team, helping customers understand the value of Box for modern enterprise applications and AI\-powered workflows.
Build: Whether it's for a demo, prototype, proof of concept, sample application, architecture pattern, or internal tool to help other Boxers get their job done, you'll be building things.
Influence: This role is extremely cross\-functional. You will have meaningful influence across the business and teams you partner with by synthesizing technical patterns, customer needs, and implementation insights back into product and engineering conversations. This includes close collaboration with Product to shape roadmap feedback, validate emerging use cases, and inform platform direction, as well as strong partnership with Core SEs to align on account strategy, technical discovery, solution positioning, and successful customer outcomes.
Grow: There's an immense opportunity for growth here\- personally, professionally, and for the team. You'll deepen your technical expertise across enterprise architecture, AI integration patterns, and solution
design.
Play: An inherent sense of curiosity and willingness to explore new technology is critical for this role. You'll get exposure to new and interesting technologies, especially in AI, and will be expected to experiment, learn quickly, and apply that knowledge in customer and internal contexts.
In this role, you will also:
- Design and validate AI\-enabled solution patterns aligned with Box's platform capabilities and product direction.
- Engage hands\-on with customer and internal teams to develop prototypes and proofs of concept that demonstrate business value and accelerate adoption.
- Create scalable technical guidance including best practices, sample apps, deployment patterns, technical playbooks, and architecture diagrams that help others build and scale with Box.
- Represent Box through demos, solution design sessions, and clear communication of Box's product vision, helping customers unlock the full value of the platform.
- Synthesize patterns across customer and solution engagements and feed insights back to Product and Engineering to help shape future roadmap and integration strategy.
- Partner closely with Core SEs throughout the sales cycle to support discovery, validate technical fit, develop differentiated solution approaches, and extend account teams with deeper AI and platform expertise.
- Work collaboratively with Product and internal technical stakeholders to test new ideas, refine solution patterns, and ensure field insights are reflected in platform priorities, enablement, and repeatable technical plays.
- Leverage AI tools and workflows to make faster, smarter decisions and increase impact.
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 make faster, smarter decisions that amplify your impact.
- A focus on becoming highly effective in Python, TypeScript, and SQL, with the ability to apply those skills to real\-world customer and product scenarios.
- Comfort with modern AI solution patterns including RAG, model selection, evaluations, tool calling, and security\-conscious solution design.
- Strong technical fluency and hands\-on ability to build, validate, and prototype solutions.
- Experience interpreting and writing code.
- Strong understanding of enterprise applications, integration patterns, and solution architecture.
- Curiosity about emerging AI technologies, tooling, and implementation patterns, with a willingness to actively learn and experiment.
- Strong communication, presentation, and cross\-functional collaboration skills, with comfort translating between technical and business audiences.
- Team\-first mindset; ability to operate effectively across Product, Engineering, and Core SE teams, balancing strategic feedback, field needs, and customer\-facing execution.
- Strategic, creative, self\-starting mindset with the ability to manage multiple initiatives and thrive in ambiguity.
- Ability to balance multiple priorities and operate independently to tackle the most impactful work.
REQUIRED SKILLS
- 2\+ years of experience in a customer\-facing Sales Engineer or Solutions Engineer role with a strong focus on platform, API, and solution\-oriented selling in SaaS, software, or related technology environments.
- Experience playing with, learning, and breaking technology.Ability to interpret and write code.
- Hands\-on technical ability to build prototypes and validate solution patterns.
- Strong communication and presentation skills.
- Experience balancing multiple priorities and operating independently.
- Team\-first mindset.
- Bachelor's degree.
PREFERRED SKILLS
- Experience with AI technologies, AI integration patterns, and emerging developer tooling.
- Demonstrated ability or strong interest in building depth in Python, TypeScript, and SQL as core languages for prototyping, integration, and solution validation.
- Comfort designing and validating modern AI application patterns including RAG, model selection, evaluations, tool calling, and security\-conscious solution design.
- Experience creating demos, proof of concepts, sample apps, technical playbooks, or architecture
guidance.
- Enterprise solution design experience across SaaS platforms, business systems, and modern application environments.
- Experience translating field insights into product feedback and roadmap influence.
- Comfort working in highly cross\-functional environments with Product, Engineering, GTM, and enablement teams.
- Strong product and customer intuition, with the ability to connect technical architecture to business value.
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 $192K-$240K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($216K) sits 20% above the category median. Disclosed range: $192K to $240K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Box AI Hiring
Box has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Redwood City, CA, US, Chicago, IL, US. Compensation range: $206K - $263K.
Location Context
AI roles in Chicago pay a median of $201,225 across 312 tracked positions.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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