Machine Learning Engineer V

What you'll do

• Design, build, and ship LLM-powered systems to simulate consumer audiences from proof-of-concept to production.
• Develop complex inference and reasoning harnesses using agentic flows, persona conditioning, and retrieval strategies.
• Fine-tune LLMs on survey and behavioral data to align models with real-world audience distributions.
• Create evaluation datasets and benchmarks to define quality metrics like distributional fidelity and behavioral validity.
• Translate research literature into functional product features in collaboration with product and engineering teams.
• Manage the full data lifecycle including curation, fine-tuning, and model alignment via SFT or preference optimization.

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.
• 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 other post-training techniques.
• Experience with RLHF, RLAIF, or RL-based state alignment of LLMs.
• Proven track record of building evaluation datasets and harnesses for model performance.
• Proficiency in Python and modern ML tools like PyTorch, Hugging Face, vLLM, and W&B.
• Knowledge of MLOps practices, pipelines, and cloud ML services such as AWS, GCP, or Azure.