Founding Machine Learning Engineer - AI Consumer Platform

Description

📍 San Francisco, CA (5 Days In-Office)
💰 $200K - $375K Base + Competitive Equity
🛂 Visa Sponsorship Available for the Right Candidate

I'm partnering with a high-growth consumer AI company that's building one of the most exciting AI-native platforms in the dating and personal connection space.

Their AI doesn't just match profiles. It combines voice transcripts, images, and structured user data to create personalized AI companions and predict human compatibility with unprecedented accuracy. Following a recent seed round and rapid user growth to 10,000 users in San Francisco, they're expanding their engineering team and hiring a Founding Machine Learning Engineer to build the recommendation engine and agentic systems behind their platform.

This is an opportunity to work on end-to-end ML systems, recommendation engines, and AI infrastructure while helping shape a product that's redefining how people find real human connections.

What You'll Do

You'll take ownership of core ML capabilities that power Known's intelligence, working directly with the former head of ML at Uber Eats and Faire.

Key responsibilities include:

• Training and deploying ML models that form the core of our recommendation engine, driving personalized matchmaking at scale.
• 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.
• Owning the end-to-end lifecycle of your models, from ideation and training to deployment and monitoring.
• Working with an ultra-personal dataset combining voice transcripts, images, and structured user data to predict human compatibility.

What We're Looking For

We're looking for engineers who have built and operated production ML systems at consumer startups - not just supported infrastructure at big tech companies.

You'll ideally have:

• 4-8 years of experience training and deploying ML models in production, leveraging PyTorch and TensorFlow.
• Experience building recommendation or matchmaking systems at consumer companies (e.g., Uber/Lyft routing, apartment finding, Google Maps routing, autonomous vehicles). NOT looking for ads ranking experience.
• Experience at an early-stage company (under 50 employees) and/or being the first or second ML hire at an early-stage company. Big tech is only acceptable if you also have early-stage startup experience.
• Experience with neural network models and applying or fine-tuning LLMs to build agentic systems or complex conversational AI.
• Experience with model deployment and basic infrastructure (e.g., Docker, Kubernetes, AWS/GCP).
• A desire to build intelligence that could lead to a million marriages and babies.
• Proficiency in Python/TypeScript and comfort with ML frameworks (PyTorch/TensorFlow).

What We're Not Looking For

Background solely in big tech

Primarily ad ranking or ad systems ML background.

Purely MLOps/infra focused without recommendation systems modeling experience.

Over-indexed on RAG/data pipelines without personalization work.

PhD-only career with no applied industry experience.

Why Join?

This is a chance to join a consumer AI company with exceptional momentum, strong product-market fit, and ambitious growth plans. They're sprinting toward Series A and have a clear path to scaling.

You'll solve challenging ML problems, work alongside a high-calibre team, and have a genuine opportunity to influence the architecture of a category-defining AI platform that helps real people find real human connections. No swiping, just real dates and proven chemistry.

If you're passionate about machine learning, recommendation systems, and building production AI platforms at consumer startups, I'd love to hear from you and please apply.