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Innovate with purpose
At BILL, we believe in empowering the businesses that drive our economy. By replacing outdated financial processes with innovative tools, we help businesses—from startups to established brands—make smarter decisions and gain control of their operations. And we don’t stop there: we’re creating the future of financial automation so businesses can spend more time on what matters.
Working here means you become part of a vision\-driven team that’s ready to tackle challenges and build cutting\-edge solutions. We value purpose, drive, and curiosity—and we thrive in a fast\-paced, ever\-changing environment. Whether in one of our offices in San Jose, CA, Draper, UT, or in a remote\-eligible role, BILLders collaborate to deliver real impact for businesses that need more time in their busy weeks.
BILL builds high performing teams and we seek to hire the best talent for every role. We're committed to building a workplace that fosters inclusion and diverse perspectives, valuing each person’s unique skills and experiences. We’d love to hear from you—you might be just what we’re looking for, whether in this role or another.
Let’s give businesses more time for what matters.
Make your impact within a rapidly growing Fintech Company
Join BILL as a Senior Staff Technical Delivery Manager embedded in our Platform, AI, and Infrastructure engineering org. This team is the foundational layer every product team at BILL builds on: backend platform services, developer tooling, AI and ML systems, and reliability engineering. The work is often invisible when it runs well and high\-impact when it does not. Keeping it healthy requires someone who can help engineering leadership make the case for foundational investment, navigate long\-horizon programs with diffuse ownership, and translate infrastructure and AI development cycles into language that business and executive stakeholders can act on.
In this role you will serve as the dedicated operational partner to the VP of Engineering for Platform, AI, and Infrastructure. The majority of your work is strategic: understanding the roadmap, engaging meaningfully on technical decisions, pressure\-testing priorities, and deploying into programs where structured execution is the difference between on\-track and off\-track. You do not need to have domain expertise in every area this org owns. What matters is the technical foundation and intellectual agility to build credibility fast, earn the trust of the org leader, and make judgment calls that hold up under scrutiny.
Responsibilities:
- Maintain a current, org\-wide view of initiatives, priorities, and delivery risks at all times, serving as the VP of Engineering's primary operational partner across the full software development lifecycle.
- Lead execution on the highest\-priority and highest\-complexity programs in the org, including long\-horizon infrastructure investments, AI and ML development cycles, and cross\-functional platform launches where diffuse ownership creates coordination risk.
- Identify blockers, dependencies, and misalignments before they become delivery risks; escalate with context and a clear recommended path forward.
- Partner with engineering leads and architects on technical decisions that carry product, timeline, or organizational implications, including helping leadership build the business case for foundational platform investment.
- Translate technical progress, infrastructure risks, and AI development timelines into clear, actionable communication for product, finance, and executive audiences.
- Build and maintain operating cadences including planning cycles, program reviews, and status reporting with a bias toward signal over noise.
- Use AI tooling and program management platforms to keep operational overhead lean without losing fidelity on what matters.
We'd love to chat if you have:
- 6 or more years in technical program management, software engineering, or a closely related field with demonstrated technical depth and a track record of leading complex, cross\-functional programs end to end.
- Strong command of the software development lifecycle, including how engineering teams scope, sequence, estimate, and deliver work across agile and hybrid delivery models.
- Technical acumen at a systems level: enough to understand how complex software systems are architected, where failure modes live, and how engineering decisions affect business outcomes, and the ability to ramp on a new domain without being hand\-held through it.
- Exceptional written and verbal communication skills, including the ability to write precisely for executive audiences and facilitate cross\-functional alignment in live settings without losing accuracy.
- Demonstrated ability to influence without authority across engineering, product, finance, and leadership, operating independently in high\-stakes environments where decisions carry real technical or organizational consequences.
- Proven experience using AI tooling and program management platforms to keep operational overhead lean without losing fidelity.
- Excellent organization skills and expert level knowledge in productivity and work management tooling.
- Practical knowledge and experience with OKR setting and tracking in a multi\-team environment.
Desired Qualifications:
- Familiarity with distributed systems, developer experience platforms, infrastructure programs, or AI and ML development cycles.
- Background in software engineering, particularly candidates who have grown toward program or product leadership over time.
- Experience at a scaling fintech or SaaS company where pace and ambiguity are the norm, including demonstrated use of AI tooling to reduce operational overhead.
Please note that this position is not eligible for visa sponsorship. Applicants must have authorization to work in the United States without requiring visa sponsorship now or in the future.
The estimated salary range for this role is noted below for our San Jose based role. Our ranges for each role and job level are based on a variety of factors including candidate experience, expertise, and geographic location and may vary from the amounts listed above. The role is also eligible for a competitive benefits package that includes: medical, dental, vision, life and disability insurance, 401(k) retirement plan, flexible spending \& health savings account, paid holidays, paid time off, and other company benefits.
San Jose pay range
$186,600 \- $233,300 USD
The estimated base salary range for this role is noted below for our office location in Draper, UT. Additionally, this role is eligible to participate in BILL’s bonus and equity plan. Our ranges for each role and job level are based on a variety of factors including candidate experience, expertise, and geographic location and may vary from the amounts listed above. The role is also eligible for a competitive benefits package that includes: medical, dental, vision, life and disability insurance, 401(k) retirement plan, flexible spending \& health savings account, paid holidays, paid time off, and other company benefits.
Draper UT pay range
$158,600 \- $198,300 USD
What’s in it for you?
Redefining how businesses automate their work is a fast\-paced, exciting, and fun environment. But we also have benefits and perks to ensure the magic isn’t only experienced by our customers, but by our employees as well.
Here is a preview of some of the amazing benefits here at BILL:
- 100% paid employee health, dental, and vision plans (choose HMO, PPO, or HDHP)
- HSA \& FSA accounts
- Life Insurance, Long \& Short\-term disability coverage
- Employee Assistance Program (EAP)
- 11\+ Observed holidays and wellness days and flexible time off
- Employee Stock Purchase Program with employee discounts
- Wellness \& Fitness initiatives
- Employee recognition and referral programs
- And much more
Don’t believe us? Check out our culture, benefits, and teams on our career site, LinkedIn Life, or YouTube pages.
BILL is an Equal Opportunity Employer. We believe our best ideas come from the unique stories, perspectives, and experiences of our team members. We welcome people of all backgrounds, abilities, and identities to bring their authentic selves and contribute to our culture.
We are committed to a transparent, inclusive hiring process that reflects our values. If you need accommodations at any stage, please contact [email protected]. To ensure a fair evaluation, our Candidate Integrity Policy prohibits the use of unapproved external assistance, including generative AI, during live interviews or assessments. Doing so will result in a review and potential disqualification.
Our Applicant Privacy Notice describes how BILL treats the personal information it receives from applicants.
Salary Context
This $158K-$233K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At BILL, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($195K) sits 10% below the category median. Disclosed range: $158K to $233K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
BILL AI Hiring
BILL has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in US. Compensation range: $233K - $238K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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