Machine Learning Engineer 5

What you'll do

• Design, build, and ship end-to-end LLM-powered systems that simulate consumer audiences.
• Develop complex inference and reasoning harnesses using agentic flows, persona conditioning, and retrieval strategies.
• Fine-tune LLMs on survey and behavioral data to align with real-world audience distributions.
• Build 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 production-ready product features for the Brand Intelligence Predict team.

What we're looking for

• 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 and familiarity with cloud ML services like AWS, GCP, or Azure.
• MS or PhD in Computer Science, Machine Learning, or a related technical field, or equivalent experience.