Data Products - Member of Technical Staff

Pilots don't train with real passengers. Actors don't rehearse with real audiences. Yet, the most consequential decisions in society are often pushed straight to production.

Simile is changing that. We have built the first AI simulation of society, populated by generative agents based on real humans. Our research pioneered the field of AI-based simulation, proving it is possible to model human behavior with high accuracy. Today, we are developing a Foundation Model to predict human behavior in any situation, at any scale.

We have raised $300M in total funding, with a $2B post-money valuation in our latest round. Our investors include Greenoaks, Index Ventures, Hanabi, A*, Bain Capital Ventures, Factory, CVS Health Ventures, and Definition.

As a Data Products - Member of Technical Staff, you will build the systems that bring real human signal into Simile. Our simulations are only as good as the data behind them. The Data Products team owns how that data is collected, structured, secured, and made useful. That includes our in‑house AI interview platform, which runs real‑time, adaptive conversations with people at scale, as well as the infrastructure that lets the world's most security‑conscious organizations trust Simile with their data.

We are looking for engineers who can bridge cutting‑edge AI and production‑grade enterprise data systems. You will design and scale products that handle sensitive, messy, real‑world data with rigor. Every system you build should be secure, auditable, and reliable by construction, while staying simple for the people who use it.

Your core responsibilities will include:

• End-to-End Ownership: Own data products from initial architecture through backend services, APIs, and user‑facing workflows. Make the core design calls on storage, orchestration, and data modeling, including when to build and when to buy.

• Data Collection & Processing: Build and scale pipelines that gather data from diverse sources, normalize it into consistent formats, and make it available to our product and research teams at scale.

• Secure Data Handling: Build systems that support the secure handling and processing of sensitive data. Implement PII redaction, tenant isolation, encryption at rest, and appropriate access controls so customers and participants can trust how their data is treated.

• Data Integration & Reliability: Design flexible paths to bring in structured and unstructured data from many sources and formats. Handle schema changes, partial failures, and edge cases without breaking downstream consumers.

• Customer & Research
  
  Collaboration:
  
  Partner closely with our researchers, product teams, and enterprise customers to turn ambiguous requirements into durable data capabilities.

• Pragmatic Shipping: Balance long‑term architectural rigor with the need to ship incrementally and learn from real‑world usage.

• Experience: 5+ years of professional engineering experience (excluding internships) at tech and product‑driven companies, including ownership of production systems that customers depend on.

• Backend & Data Proficiency: Strong command of Python and/or Type Script, plus solid SQL and data modelling fundamentals. Experience with relational databases (e.g., Postgres) and cloud infrastructure (AWS, GCP, or Azure).

• Data Systems
  
  Experience:
  
  Hands‑on experience building data pipelines or integrations, including batch and/or streaming processing, workflow orchestration (e.g., Temporal), and schema evolution.

• Security Mindset: Practical experience with authentication and authorization, encryption, secrets management, multi‑tenancy, and handling sensitive data in enterprise or regulated environments.

• Product Thinking: A track record of balancing technical…