Founding Engineer (AI)

Momentic•San Francisco, CA

2d•$150,000 - $220,000•Onsite

About The Position

At Momentic, we’re building the future of quality. We’re building the all-in-one quality platform powered by state of the art agents to help our customers ensure quality at every stage of the SDLC. Top engineering teams at companies like Notion, Retool and Webflow use Momentic to ship high quality product. Millions of Momentic tests are executed every single day. Our product has a very large problem space so there’s a ton of stuff to build and take ownership of - we’d be excited to get your help as we're hiring several extremely talented software engineers across the stack. The AI-native automated testing platform is incredibly sticky. Our customers run us on every pull request and merge and before each deploy as quality gates. It is leagues ahead of the status quo today (think Selenium/Cypress/Playwright). Check out the demo video on our website.

Requirements

Experience with LLM performance tuning, including prompt engineering and context management strategies.
Experience with LLM evaluations and observability.
Experience integrating LLMs into real-world applications using a modern tech stack (Python, TypeScript, etc.).
Familiarity and experience with supervised fine-tuning (SFT) and/or reinforcement learning fine tuning (RLFT) on platforms such as fireworks, Together AI, or Baseten.
Familiarity with evaluation dataset engineering, prompt engineering, LLM as a judge verifiers, and RL environments.
3+ years of experience.
Strong engineering fundamentals, efficient coding practices.
Ability to work autonomously, own details, and project manage own work.
Willingness to work in-person in San Francisco or relocate.

Nice To Haves

Experience with supervised or reinforced fine-tuning.
Experience with classical machine learning techniques such as template matching, bounding box detection, and OCR.
Experience running statistical experiments.

Responsibilities

Build and take ownership of features for an AI-native automated testing platform.
Integrate LLMs into real-world applications using a modern tech stack.
Tune LLM performance, including prompt engineering and context management strategies.
Conduct LLM evaluations and implement observability.
Engage in supervised fine-tuning (SFT) and/or reinforcement learning fine tuning (RLFT).
Engineer evaluation datasets, utilize prompt engineering, and implement LLM as a judge verifiers and RL environments.
Contribute to running statistical experiments.
Work in-person in San Francisco and contribute to building a positive company culture.