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.
Must-Have Experience
We want someone who has built systems that learn from experience — not just analyzed history.
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.
Big Pluses
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
How You’ll Work (AI-Native Collaboration)
You’ll partner closely with:
AI / NLP — translating signals into agent behavior
Product — defining success over long time horizons
Infrastructure — building reliable, observable learning pipelines
Leadership — aligning learning with business strategy
You won’t just evaluate results. You’ll design how the system learns from them.
Top Skills
What We Do
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