Founding ML Engineer

Founding ML Engineer

San Francisco, CA

Up to $375K + Equity

My client is a VC backed consumer AI Start Up., They recently raised a $10m seed funding from top tier investors and are on track for Series A later this year, you will be joining an elite 15 person team with a working product, you'd be scaling something that's already validated, not building from zero.

They are looking for founding machine learning engineers to continue to design and build the core systems intelligence, driving our recommendation engine and agentic systems. This is a unique opportunity to work with an ultra-personal data-set, combining voice transcripts, images, and structured user data to create both personalized AI companions as well as predict human compatibility. You’ll work directly with Chen Peng, former head of ML at Uber Eats and Faire.

Requirements

• 4 - 8 years of experience training and deploying ML models in production
• Built recommendation or matchmaking systems at consumer companies - examples include Uber/Lyft routing, apartment finding, Google Maps routing, even autonomous vehicles are relevant.
• Experience at early- stage company (under 50 employees) and/or the first or second ML hire at an early- stage company
• Has both applied experience AND some research/publication background
• End- to- end ML model training, deployment, and monitoring
• Proficiency in Python/TypeScript; comfortable with ML frameworks (PyTorch/TensorFlow)
• Experience with structured data pipelines from unstructured/multimodal inputs
• Experience applying or fine- tuning LLMs for agentic systems
• Must work on- site in San Francisco 5 days a week

What you’ll do

It’s up to you to decide what part of the ML stack you’re most excited about working on.

This could be:

• Training and deploying ML models that form the core of our recommendation engine
• Designing evals to assess recommendation ability and RL systems to learn from results data
• Building personalization and long-term memory systems into Known’s conversational AI
• Using LLMs to enhance our suite of user facing AI Agents

You will own the end-to-end lifecycle of your models, from ideation and training to deployment and monitoring.

Why candidates should join

• One of the few truly consumer AI companies right now — raised a $9.7M seed from top-tier investors (Forerunner, NFX, Pear VC) and is sprinting toward a Series A in the fall. You'd be joining at the ground floor of a rocketship.
• Featured by OpenAI and likely Anthropic — Known was highlighted in OpenAI's features campaign for novel uses of their tooling and is benchmarking both OpenAI and Anthropic models on the frontier domain of matchmaking.
• 70% mutual acceptance rate — The product is already working. With 10k+ users in SF and two more markets launching before the fall, you'd be scaling something that's already validated, not building from zero.
• Work directly with elite ML leadership — You'd pair with Chen Peng (former head of ML at Uber Eats and Faire) and another senior ML lead who was pre-IPO at Airbnb. The caliber of people you'd learn from and build alongside is exceptional for a 15-person team, and the team prides itself on a flat structure.