Founding Machine Learning Engineer

San Francisco, United States
Full-time
Visa Sponsorship

About the role

About the Role

We’re looking for founding machine learning engineers to continue to design and build Known’s 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.

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.

Requirements

  • 4-6 years experience training and deploying ML models in production, leveraging PyTorch and TensorFlow
  • Applying or fine-tuning LLMs to build agentic systems or complex conversational AI
  • Experience with neural network models
  • Experience with model deployment and basic infrastructure (e.g., Docker, Kubernetes, AWS/GCP)
  • You want to build intelligence that could lead to a million marriages and babies

About Known

Known is an AI-powered matchmaking company on a mission to empower humanity by applying general intelligence to human connection. Unlike traditional dating apps that rely on swiping, Known takes a fundamentally different approach — users join by telling their life story through a conversation with an AI voice agent (averaging 27 minutes), giving the platform uniquely intimate context to make meaningful matches.

Known handles everything from finding your match to planning your date, removing the friction from the user and transferring it to the platform. The result: real dates with proven chemistry, no swiping required.

Culture & Team

Known is a tight-knit team of 18 based in Cow Hollow, San Francisco. The company is engineering-heavy, founded by builders behind some of the most widely used AI-driven consumer products including Uber Eats, Uber, and Afterpay. The culture blends technical excellence with the social energy you'd expect from a consumer dating brand — rooftop cocktail nights, team off-sites, and a genuinely fun, young environment where everyone is social and friendly.

Backed by a $10M seed round from Forerunner Ventures, NFX, and Pear VC, Known is sprinting toward a Series A and actively launching in new cities across the US. The company has already amassed 10,000+ users in San Francisco and is expanding to San Diego, LA, New York, and Chicago.

Required skills

Python
TypeScript
PyTorch
TensorFlow
Docker
Kubernetes
AWS/GCP
LLMs (OpenAI, Anthropic)

Other roles at Known

Interested?

Let me introduce you to the founders.

Skip the process

Job details

Salary

$200,000 - $375,000

Location

San Francisco, United States

Experience

4+ years

Company

NameKnown
Industryai, consumer, marketplace
Team Size18

Funding

Total raised

$9.7M

Last stage

Seed

Investors

Forerunner Ventures
NFX
Pear VC
CCoelius Capital

Founders

Joseph Zizzo

Joseph Zizzo

Co-Founder / CTO

LinkedIn
KS

Kern Schireson

Chairman and CEO

LinkedIn
BR

Brad Roth

Co-Founder, President of Known Studios / Known Originals

MF

Mark Feldstein

Co-Founder, President of Known Studios

Asher Allen

Asher Allen

Co-Founder

LinkedIn
Celeste Amadon

Celeste Amadon

Co-Founder & CEO

LinkedIn

What happens next.

No applications, no recruiter spam. Just the intro.

01

Confirm the fit

A few questions to make sure this role is the right shape for you. Two minutes.

02

I pitch you to the company

I write the intro, send it to the founder, and handle the back-and-forth.

03

A meeting lands on your calendar

If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.