Lead Data Scientist (Agentic AI)

Ditto AI•San Francisco, CA

About The Position

Ditto is building the agentic social network — a platform where profiles aren’t static pages, but AI agents that learn from experience, adapt, and help people form meaningful connections. As an AI-native company, Ditto is designed to operate as a continuously improving intelligence: agents learn from real behavior systems evolve through feedback safety, alignment, and control come first We believe that systems that learn through interaction will outperform systems trained only on static human data — and unlock a new level of meaningful human connection. Role Overview We’re hiring a Lead Data Scientist (Data Engine) to design and own the learning backbone that allows Ditto’s agents to improve safely over time. You will build systems that: capture continuous experience streams, not snapshots transform signals into rewards grounded in real outcomes feed those rewards back into agents prevent drift while still allowing improvement help the system reason and plan based on consequences, not guesswork This role owns the full loop: data → experience → reward → feedback loops → discovering leverage points → adaptation → product outcomes You are here to build a living system that learns — continuously — and to identify small, high-leverage changes that create outsized impact over time. What You’ll Build You will architect the data engine that powers experiential learning: experience streams — behavior stitched across long time horizons reward streams — derived from actions, outcomes, and environment feedback models that capture intent, preference, habit, and change evaluation pipelines that measure long-term improvement, not one-off wins matchmaking & recommendation signals that uncover hidden compatibility systems that let agents plan based on predicted consequences experimentation frameworks (A/B tests, bandits, sequential testing) drift detection & safety monitors guardrails to prevent reward hacking, bias loops, or unintended behaviors Everything must be auditable, grounded, explainable, repeatable. Systems Thinking Expectations (Why This Role Is Different) You will: design reinforcing loops that compound value responsibly design balancing loops that stabilize trust, fairness, and safety identify and avoid system traps (gaming metrics, tragedy-of-the-commons patterns) push on leverage points that change behavior — not just parameters Sometimes the right move is not tuning a metric — it’s redefining the goal.

Requirements

10+ years in applied ML / data science (production)
3+ years building LLM-enabled systems
built behavioral pipelines that drive real agent / product behavior
designed feedback & reward loops end-to-end
hands-on large-scale data engineering
deep, practical experience with agent frameworks, including: LangGraph (preferred) LangChain or equivalent agent-orchestration frameworks in production
experience feeding data back into agents to actually change behavior
strong grounding in: reward shaping value estimation world modeling temporal / TD learning long-horizon feedback loops
If your work stops at insights, this role will feel wrong. If your systems adapt and improve — you’ll thrive here.

Nice To Haves

social graphs, matchmaking, recommendation systems
trust & safety, anomaly detection, abuse prevention
causal inference / world-model thinking
reinforcement learning or TD-style learning
experience grounding rewards in real outcomes, not proxy metrics

Responsibilities

architect the data engine that powers experiential learning
design reinforcing loops that compound value responsibly
design balancing loops that stabilize trust, fairness, and safety
identify and avoid system traps (gaming metrics, tragedy-of-the-commons patterns)
push on leverage points that change behavior — not just parameters

Stand Out From the Crowd

Upload your resume and get instant feedback on how well it matches this job.

Upload and Match Resume