Staff / Senior Machine Learning Engineer (LLM), AI Lab

Our co-founders started Zip in 2020 to address this seemingly intractable problem with a purpose-built platform that provides a simple, consumer-grade user experience. Within just a few short years, Zip created the procurement orchestration category and developed the leading solution in this $50B+ TAM space. Today, leading companies like Instacart, Anthropic, Sephora, Discover, Reddit, and Lyft rely on Zip to manage billions of dollars in spend.

We're a fast-growing team that helped scale category-defining companies like Airbnb, Meta, Salesforce, Databricks, Apple, Google, and Ramp. With a $2.2 billion valuation and $370 million in funding from Y Combinator, BOND, DST Global, and CRV, we’re focused on developing cutting-edge technology, expanding into new global markets, and—above all–driving incredible value for our customers. Join us!

Your Role

Zip just launched a suite of AI functionalities in various areas such as AI document extraction, AI assistant, and AI risk detection. We view AI as a key strategic investment to realize our mission of helping enterprises procure faster, smarter, and safer.  

You Will

As a Staff / Senior Machine Learning Engineer for our AI Lab, you will further accelerate our new AI product development as well as improve our AI model accuracy. You’ll work closely with other engineers and cross-functional partners (e.g. PMs, designers, and sales team) to identify opportunities for business impact, understand, refine, and prioritize requirements for Zip AI products and models, drive engineering decisions, and quantify impact. Some examples include: Zip AI: AI assistant, document extraction, risk detection, intake automation, vendor consolidation, and RFx survey generation.

Please note, that we are looking for someone who will be able to come into our downtown San Francisco office on a hybrid basis (3 days / week).

Qualifications

• 4+ years of experience designing and developing Machine Learning models

• Bachelor’s and/or Master’s degree, preferably in CS/ML or related fields

• Proficiency in software development languages (Python/Java/C++ or equivalent) and data engineering skills

• Deep understanding of Machine Learning best practices (e.g., training/serving, feature engineering, feature/model selection, imbalance data) and algorithms (e.g. deep learning, optimization)

• Experience with Tensorflow or PyTorch

• Industry experience in designing and productionizing end-to-end machine learning models (e.g. search ranking, recommendation, personalization, NLP)

• Exposure to architectural patterns of large, high-scale software applications (e.g. search, recommendation)

• Familiarity with LLM and GenAI concepts and use cases

• Strong product / business sense and can drive product decisions 

• Great cross-functional collaboration skills

Preferred Qualifications

• Experiences with NLP, LLM, chatbot, or RAG development

• Experiences with designing and productizing ML systems from the ground up

• Experiences with model serving systems such as TF Serving or Torch Serve

The salary range for the Senior/Staff role is $150,000 - $250,000. The salary for this position is determined based on a variety of job-related factors that may include leveling, relevant experience, education, or particular skills and expertise.

Perks & Benefits

At Zip, we’re committed to providing our employees with everything they need to do their best work.

• 📈  Start-up equity

• 🦷  Full health, vision & dental coverage

• 🍽️  Catered lunches & dinners for SF employees

• 🚍  Commuter benefit

• 🚠  Team building events & happy hours

• 🌴  Flexible PTO

• 💻  Apple equipment plus home office budget

• 💸  401k plan

We're looking to hire Zipsters and that means hiring people who take ownership, communicate openly, have an underdog mindset, and are excited to increase the pace of innovation for every business in the world. We encourage all candidates to apply even if your experience doesn't exactly match up to our job description. We are committed to building a diverse and inclusive workspace where everyone (regardless of age, religion, ethnicity, gender, sexual orientation, and more) feels like they belong. We look forward to hearing from you!