Staff Machine Learning Engineer

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 By replacing outdated financial processes with innovative tools, we help businesses—from startups to established brands—make smarter decisions and gain control of their operations.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. We're remote-first, with office spaces in San Jose, CA and Draper, UT — and opportunities to connect in person when it counts. 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 at a leading Fintech Company

Join BILL's AI Product Engineering team and help shape the future of intelligent financial automation. BILL processes millions of financial documents every month, and machine learning sits at the core of how we turn unstructured documents into accurate, actionable financial data. We are looking for a Staff ML Engineer to raise the technical bar across our AI/ML organization, someone who is equally comfortable getting hands-on with model training code and setting the multi-quarter technical direction for a team.

This is a senior individual-contributor role with organization-wide influence. You will architect and build the next generation of our ML systems, spanning LLM-based extraction, fine-tuned open-weight models, intelligent routing, and the data foundations underneath them, while mentoring engineers and shaping how we evaluate, deploy, and iterate on models in production.

Responsibilities:

• Own technical direction for high-impact ML initiatives end to end, from problem framing and research exploration through production deployment and measurement, across document understanding, field extraction, and model-serving infrastructure
• Stay hands-on: prototype, fine-tune, and ship models yourself; write production code alongside the engineers you mentor
• Lead our LLM strategy, including fine-tuning open-weight foundation models, designing evaluation harnesses, and making principled build-vs-buy decisions between self-hosted and frontier API models across accuracy, cost, latency, and privacy constraints
• Bring a research-oriented mindset to production problems: read and translate current literature into practice, design rigorous experiments and ablations, and know when a published technique will or won't transfer to our data distribution
• Design the data flywheel, including labeling pipelines, human-in-the-loop feedback, and drift monitoring, so our models improve continuously from production signals
• Set engineering standards for model evaluation, calibration, reproducibility, and responsible deployment; be the person others seek out for design reviews on anything ML-shaped
• Mentor and multiply: grow senior and mid-level ML engineers through code review, pairing, and technical guidance; influence roadmaps in partnership with product and platform leadership

We’d love to chat if you have:

• Requires a minimum of 8 years of related experience with a Bachelor's degree; or 6 years and a Master's degree; or a PhD with 3 years of experience; or equivalent experience
• 8+ years in software/ML engineering with a demonstrated track record at Staff level or equivalent scope, leading technically while remaining deeply hands-on with the modern ML/AI stack (PyTorch, distributed training, experiment tracking, feature and vector stores)
• Direct experience fine-tuning LLMs, including supervised fine-tuning, LoRA/QLoRA, preference optimization (DPO/RLHF), and structured-output training, and taking fine-tuned models to production at scale
• In-depth knowledge of open-weight and foundation models: Experience with document AI / intelligent document processing (OCR pipelines, layout-aware models such as LayoutLM/Donut, vision-language models)
• Production experience in at least one of: recommendation systems, personalization, or search/ranking; you have built systems that learn from user behavior at scale, handled feedback loops and position bias, and shipped models where offline metrics had to survive contact with online reality
• Strong data reasoning fundamentals: sampling and class imbalance, evaluation design and metric selection, confidence calibration, leakage detection, distribution shift, and the statistical judgment to know when an A/B result or benchmark number is real versus noise
• Research-oriented mindset with shipping discipline: you follow the literature, form hypotheses, and run disciplined experiments, but you measure success by what reaches production and moves business metrics

Nice-to-Have

• Experience with document AI / intelligent document processing (OCR pipelines, layout-aware models such as LayoutLM/Donut, vision-language models)
• Publications, patents, or open-source contributions in ML
• Experience with privacy-constrained ML (on-prem/VPC model hosting, PII handling, data governance in fintech or healthcare)
• Experience designing multi-model routing or cascade architectures balancing cost, latency, and accuracy

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 below. 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. The estimated salary ranges noted below roles in the specific geographic zones

Zone 1: San Francisco Bay Area CA (includes HQ), New York City, Seattle, Los Angeles County $195,000—$233,800 USD Zone 2: CA (Non San Francisco Bay Area and Los Angeles County), Austin TX, Massachusetts $175,500—$210,400 USD Zone 3: Utah (includes Utah office), Dallas TX, Houston TX, Florida, North Carolina, Illinois, Colorado, Arizona, Georgia, Oregon, Pennsylvania $165,800—$198,700 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 . 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.

Originally posted on Himalayas