4-6 years of experience in training and deploying machine learning models in production
Proficient in PyTorch and TensorFlow for model development
Skilled in applying or fine-tuning large language models to create conversational AI
Experience with neural network architecture
Familiar with model deployment using Docker, Kubernetes, and AWS/GCP
Responsibilities
Design and build core systems intelligence for the recommendation engine
Train and deploy machine learning models integral to personalized user experiences
Create evaluation metrics to assess recommendation performance and learn from results
Integrate personalization features and long-term memory into the conversational AI
Utilize large language models to refine user-facing AI agents
Oversee the end-to-end lifecycle of machine learning models from ideation to monitoring
Benefits
High degree of autonomy and ownership in projects
Opportunity to work with a unique, intimate dataset
Join a team with a proven record of building widely-used AI-driven consumer products
Engage directly with experienced leaders in machine learning
Be part of a mission-driven company aiming to enhance human connections
Full Job Description
Known - Founding Machine Learning Engineer
San Francisco, CA (In-Person)
200k-375k Cash + Equity
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
Our Investors
We're backed by Eurie Kim and Kirsten Green at Forerunner Ventures (the investors behind Decagon, Faire, and Oura), NFX and PearVC.
