Back to jobs

Member of the Technical Staff — AI/ML

San Francisco · New York City · San Francisco, California, United States

Open 5 months, first seen by us 164 days ago
Pay
Pay not listed
Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting
Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting
Team
Engineering — team source
Department: Engineering
From the employer’s posting

What you’ll work on

Full posting
  • Build tool-using LLM agents that surface insights and execute approved tasks

  • Refine invoice matching, payment reconciliation, and financial document processing

  • Apply and optimize LLMs and RAG systems for financial use cases

  • Build reliable, cost-efficient AI pipelines from ingestion to inference

  • Set up golden datasets, agent tracing, and A/B testing to ship with confidence

From the employer’s posting
Build tool-using LLM agents that surface insights, recommend next steps, and execute approved tasks
Refine capabilities like invoice matching, payment reconciliation, and financial document processing
Apply and optimize LLMs and RAG systems for financial use cases, including fine-tuning on proprietary data where it moves the needle
Build robust AI pipelines from ingestion to inference — reliable, maintainable, and cost-efficient through smart model routing
Stand up golden datasets, agent tracing, regression-on-PR, and A/B testing so we ship confidently and catch silent regressions

About Stuut

Stuut is the AI coworker that runs order-to-cash for the world’s leading enterprises.

In the employer’s words · Read in context · Company website ↗

Apply at Stuut

Posting record

Report an error

Open 5 monthsWe first saw this posting 164 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.

Status in our records
Live
First seen by us
Jun 2, 2026164 days ago
Recorded sightings
91
Last seen by us
Nov 13, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 164

Up longer than most. Similar jobs usually come down between day 4 and day 77.

From 51 similar postings that came down. Coming down is not always a hire.

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • PyTorch
  • scikit-learn
  • XGBoost
Where the posting names them
- Understand MLOps fundamentals: deployment, monitoring, A/B testing, model and prompt versioning, feature stores - Are fluent in Python and modern AI/ML tooling (PyTorch, Transformers, scikit-learn, XGBoost, vLLM, LangChain/LlamaIndex, or equivalent) - Have shipped AI/ML products that solved real business problems, not just prototypes

Your next step

Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.

Applying through Ashby

  • No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.

Already applied? Track this application

Demand for AI Engineer roles

New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.

Weekly new AI Engineer postings at the same employers, from the week of August 17 to the week of November 2. The fewest in a week was 7, the most 65, and the latest week had 65.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 6 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under AI Engineer, by the work the posting describes.

AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.

Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.

An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.

Job description

View original posting ↗

About Stuut

Stuut is the AI coworker that runs order-to-cash for the world’s leading enterprises. Across order management, credit, collections, payments, cash application, disputes and deductions, Stuut does the work end to end and keeps going until the cash hits the bank.

Over 150 companies, spanning the mid-market to the Fortune 50, including Honeywell, ZoomInfo and PerkinElmer, use Stuut to unlock up to 40% more cash flow and cut DSO by 47%. More than $3 billion has moved through the platform to date.

Founded in New York by Tarek Alaruri and Ben Winter, Stuut has raised $93 million from Insight Partners, Andreessen Horowitz, M12, Microsoft’s Venture Fund, and others. Learn more at stuut.ai.

The Role

We’re hiring a Member of Technical Staff – AI/ML to design, build, and deploy AI-powered systems that solve real-world financial operations challenges. You’ll take state-of-the-art AI research and translate it into production-grade features that deliver measurable customer impact. From intelligent invoice matching to automated payment reconciliation, you’ll create scalable, reliable AI applications that integrate seamlessly into our platform.

This is a hands-on role for an engineer who thrives at the intersection of AI innovation and practical business application — turning cutting-edge models into real-world value for mid-market CFOs

What You’ll Do

  • Create production-ready AI applications and agentic systems that address customer financial workflow challenges

  • Build tool-using LLM agents that surface insights, recommend next steps, and execute approved tasks — not just chatbots

  • Refine capabilities like invoice matching, payment reconciliation, and financial document processing

  • Apply and optimize LLMs and RAG systems for financial use cases, including fine-tuning on proprietary data where it moves the needle

  • Build robust AI pipelines from ingestion to inference — reliable, maintainable, and cost-efficient through smart model routing

  • Stand up golden datasets, agent tracing, regression-on-PR, and A/B testing so we ship confidently and catch silent regressions

  • Partner with Product, Engineering, Data, and customers to translate business needs into AI solutions

  • Treat every AI feature as a continuously-improving system — instrument everything, iterate

You Might Be a Fit If You…

  • Have 5+ years of AI/ML experience

  • Have shipped agentic products in production and understand the failure modes (tool use, planning, state, recovery, human-in-the-loop)

  • Have integrated and fine-tuned LLMs, and built RAG systems for document- or data-intensive workflows

  • Have trained and deployed classical ML models (risk scoring, forecasting, ranking, or similar) — feature engineering, model selection, evaluation, calibration

  • Have strong opinions on AI/ML evals — golden datasets, offline + online evaluation, statistical significance, evals in CI

  • Are familiar with LLM observability tooling (LangSmith, Braintrust, Arize, or similar) and treat tracing as table stakes

  • Understand MLOps fundamentals: deployment, monitoring, A/B testing, model and prompt versioning, feature stores

  • Are fluent in Python and modern AI/ML tooling (PyTorch, Transformers, scikit-learn, XGBoost, vLLM, LangChain/LlamaIndex, or equivalent)

  • Have shipped AI/ML products that solved real business problems, not just prototypes

  • Can translate business requirements into clear technical solutions

  • Bonus: experience with model routing across providers

  • Bonus: fintech, B2B SaaS, or AR/AP domain experience

  • Are plugged into the AI/ML community and energized by bringing AI to real-world use cases

US Compensation

  • Top-of-market salary and equity package

  • Benefits (for U.S.-based full-time employees)

  • Medical, dental & vision insurance coverage for you

  • 401(k) & Match

  • Equity

  • Flexible PTO

  • Parental Leave

Stuut is an equal opportunity employer committed to building a team with diverse backgrounds, perspectives, and experiences. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, genetic information, or any other characteristic protected by applicable law.

We are also committed to providing reasonable accommodations throughout the application and interview process. If you need any accommodations, please do not hesitate to reach out.

Source details

View original posting ↗

Source notes

Source excerpts

Selected passages from the saved posting. Check the full description for conditions and exceptions.

Pay

No pay amount identified in the saved description.

Location & working pattern

San Francisco, California, United States

Working pattern and location restrictions need checking in the full posting.

Work authorization

No clear work-authorization passage found. Eligibility is unconfirmed.

Find answers in the posting

AI
How answers work

AI selects complete passages from this posting. Check them for conditions and exceptions.

Uses this posting and your question. No profile needed.