About this role
An experienced practitioner who thrives at the intersection of hands-on model development and high-level technical leadership will find a natural home in this role. Working within the AI Product Engineering team at BILL, you will help shape intelligent financial automation systems that process millions of unstructured documents every month. The team focuses on building next-generation machine learning architecture, including large language model extraction, fine-tuned open-weight models, and robust data foundations.
Operating as a senior individual contributor, you will own ML initiatives end-to-end while mentoring engineers across the organization. You will spend your time writing production code, designing evaluation harnesses, and establishing engineering standards for calibration and deployment. Anyone who combines deep research curiosity with a strict discipline for shipping models that move business metrics will excel here.
What you will do
- Own the technical direction for high-impact machine learning initiatives from initial research exploration through to production deployment and measurement.
- Remain hands-on by prototyping, fine-tuning, and shipping models while writing production code alongside mentored engineers.
- Lead the organization's LLM strategy through fine-tuning open-weight foundation models, designing evaluation harnesses, and making build-versus-buy decisions.
- Translate current academic literature into practical applications by designing rigorous experiments and ablations.
- Design comprehensive data flywheels that incorporate labeling pipelines, human-in-the-loop feedback, and drift monitoring.
- Establish engineering standards regarding model evaluation, calibration, reproducibility, and responsible deployment.
- Mentor and grow senior and mid-level machine learning engineers through code reviews, technical pairing, and strategic guidance.
What you need
- Demonstrate a minimum of 8 years of related experience with a Bachelor's degree, 6 years with a Master's degree, or a PhD with 3 years of experience.
- Bring 8 or more years of software or machine learning engineering experience with a track record at the Staff level or equivalent scope.
- Possess direct experience fine-tuning LLMs using supervised fine-tuning, LoRA/QLoRA, preference optimization, and structured-output training at scale.
- Show production experience in recommendation systems, personalization, or search and ranking with a history of handling user behavior loops.
- Apply strong data reasoning fundamentals covering class imbalance, evaluation design, confidence calibration, and distribution shift.
- Maintain a research-oriented mindset paired with a strong discipline for shipping production-ready code.
Nice to have
- Experience with document AI and intelligent document processing such as OCR pipelines and layout-aware models
- Publications, patents, or open-source contributions in machine learning
- Experience with privacy-constrained machine learning including on-premises hosting and PII handling in fintech or healthcare
- Experience designing multi-model routing or cascade architectures balancing cost, latency, and accuracy
Benefits
- Medical, dental, and vision plans
- Health Savings Account and Flexible Spending Account
- Life insurance and long and short-term disability coverage
- 401(k) retirement plan
- Paid holidays, wellness days, and flexible time off
- Employee Stock Purchase Program
Skills and tech stack
Questions candidates ask
What is the remote work policy?
The company operates on a remote-first basis with office spaces available in San Jose, California and Draper, Utah. Employees have opportunities to connect in person when it counts.
What is the salary range for the role?
The estimated salary ranges vary by geographic zone, spanning from $165,000 to $233,800 USD based on factors such as candidate experience, expertise, and location.
What kind of projects will I work on?
You will architect and build the next generation of machine learning systems spanning LLM-based extraction, fine-tuned open-weight models, intelligent routing, and underlying data foundations.
What are the expectations for technical leadership?
This is a senior individual-contributor role with organization-wide influence where you will set multi-quarter technical direction and mentor senior and mid-level machine learning engineers.