Pear VC
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.
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.