AI / ML Engineer - Known
At a glance
Mid-level Machine Learning Engineering role at Pear VC. Austin · full-time · no salary stated · we estimate $142,875–$228,150 across the United States.
Pear VC has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build multi-step matching models that retrieve and rerank profiles for compatibility, search and personalization
- Create ML workflows for preparing data, generating features, training models, evaluating results and serving predictions
- Prototype and launch voice and text agents for intake interviews, matchmaking and conversations
- Deploy models with safeguards, then monitor performance, fairness and safety in production
- Test changes offline and with users, measuring engagement, match success and conversation quality
What you bring
- At least three years in applied machine learning or data science engineering, ideally on recommendations, search or personalization
- Strong Python skills and experience with a modern ML framework such as PyTorch, TensorFlow or JAX
- Hands-on work with LLMs, embeddings and agent-based workflows
- You understand A/B testing and how people can help evaluate models in production
Who this fits
This hands-on role suits someone ready to take ownership from research and prototypes through production, working with product, platform and design teammates. It is based on-site in Austin.
From the employer
About the Role
You’ll be the technical founder driving the machine learning and AI backbone behind Known — an intelligent, compatibility-driven dating platform that blends psychology, data, and human-like conversation. You’ll design and ship the systems that make Known feel magical: personalized matching algorithms, adaptive recommendation loops, and natural voice/LLM-based interactions that help users connect meaningfully.
You’ll work closely with the founding team (product, platform, and design) to shape both the data and ML foundations and the user-facing experiences that differentiate Known. This is a hands-on role with ownership across research, prototyping, and production deployment.
Responsibilities
Design and implement multi-stage matching systems (embedding-based retrieval + LLM re-ranking) for compatibility scoring, search, and personalization.
Develop and maintain ML pipelines for data ingestion, feature generation, model training, evaluation, and inference.
Prototype and productionize agentic workflows for natural-language and voice interactions (e.g., AI-assisted intake interviews, voice matching, or conversation agents).
Deploy and monitor ML models in production with guardrails for performance, fairness, and safety.
Run offline & online experiments (A/B and multivariate) to measure real-world outcomes such as engagement, match success rate, and conversation quality.
Collaborate cross-functionally with platform engineers and product designers to integrate AI seamlessly into the Known user experience.
Requirements
3+ years in applied ML or data science engineering roles, ideally working on recommendation, search, or personalization systems.
Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, JAX, Hugging Face).
Experience with LLMs, embeddings, and agentic workflows.
Understanding of A/B testing and human-in-the-loop system design for model evaluation in production.
Familiarity with ANN search systems and modern MLOps tools is a plus.
Reinforcement learning or preference modeling experience is a strong plus.
You care about building safe, fair, and human-centered AI experiences.
Example Projects
Develop a user matching system based on profile information, onboarding transcripts and engagement behavior.
Build a dynamic profile enrichment pipeline that integrates behavioral and linguistic features into user representations.
Deploy a lightweight LLM-powered voice agent for user intake and conversational matchmaking.
Create an evaluation harness combining offline metrics (AUC, NDCG) and online experiments (match acceptance, message rate).
Build model monitoring and retraining loops informed by live interaction feedback.
Why This Role
This is an opportunity to define the technical DNA of a consumer AI product from day one — to architect and deploy systems that combine data science, human psychology, and generative AI. Your work will directly shape how people connect, communicate, and build relationships in an AI-assisted world.