AI / ML Engineer at Known (San Francisco, CA)AI / ML Engineer

• San Francisco, CA (In-Person)
• 200k-375k Cash + Equity

Known is a matchmaker that talks to users and supports them like a friend. Our mission is to empower humanity by applying general intelligence to human connection.

Users join Known by telling us their life story. On average, our new users talk to our AI voice agent for 27 minutes, giving us a uniquely intimate multi-modal data set.

We are a team of engineers who’ve created some of the most widely used AI-driven consumer products including Uber Eats, Uber, Faire and Afterpay.

We love to work hard, with a high degree of autonomy and ownership. We work together in Cow Hollow, San Francisco.

Learn more

• Known apps.apple.com/id6748926392
• Our Launch x.com/2018023250408960293
• Known’s 10M Seed | TechCrunch techcrunch.com
• “You Don’t Need to Swipe Right” - Known | NYT nytimes.com/ai-dating-apps.html
• Known | FastCompany fastcompany.com
• Website knowndating.com

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.

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.

• 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

We’re backed by Eurie Kim and Kirsten Green at Forerunner Ventures (the investors behind Decagon, Faire, and Oura), NFX and PearVC.