Senior Applied Scientist

What you'll do

• Build and ship end-to-end LLM-powered systems that simulate consumer audiences for brand testing.
• Develop complex inference harnesses using agentic flows, persona conditioning, and retrieval strategies.
• Fine-tune LLMs on survey and behavioral data to align with real-world audience distributions.
• Create evaluation datasets and benchmarks to measure distributional fidelity and behavioral validity.
• Manage the full data lifecycle from curation through evaluation for synthetic audience models.
• Translate research literature into shippable product features in collaboration with engineering teams.

What we're looking for

• MS or PhD in Computer Science, Machine Learning, or a related technical field, or equivalent experience.
• Substantial hands-on experience building LLM-based applications in production.
• Demonstrated experience designing and shipping complex inference harnesses including agentic systems, structured reasoning, and RAG.
• Hands-on experience fine-tuning LLMs using SFT, preference optimization (DPO/GRPO), and modern post-training tradeoffs.
• Experience with RLHF, RLAIF, or RL-based state alignment of LLMs.
• Proven track record of building evaluation datasets and harnesses.
• Proficiency in Python and strong grounding in data structures, algorithms, and modern ML tooling like PyTorch and Hugging Face.
• Hands-on knowledge of MLOps practices, pipelines, and cloud ML services (AWS, GCP, Azure).
• Experience shipping a customer-facing Gen AI feature from proof-of-concept to production end-to-end.
• Prior work on synthetic audiences, persona simulation, or LLM-based human behavior modeling (preferred).
• Familiarity with synthetic audiences research literature such as silicon samples or generative agents (preferred).
• Experience with public opinion, survey data, or panel-based consumer research data (preferred).