Machine Learning Engineer Jobs

About The Position

Requirements

• Substantial hands-on experience building LLM-based applications in production.
• Demonstrated experience designing and shipping complex inference harnesses on top of large language models (agentic systems, structured reasoning, sampling/decoding strategies, RAG).
• Hands-on experience fine-tuning LLMs with techniques including 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 — you have opinions about what makes an eval load-bearing versus theater.
• Proficiency in Python and strong grounding in data structures, algorithms, and modern ML tooling (PyTorch, Hugging Face, vLLM, W&B or equivalents).
• Hands-on knowledge of MLOps practices and pipelines.
• Familiarity with cloud ML services (AWS, GCP, Azure).
• Shipped a customer-facing Gen AI feature from proof-of-concept to production end-to-end.
• MS or PhD or equivalent experience in Computer Science, Machine Learning, or a related technical field, or equivalent experience.

Nice To Haves

• Prior work on synthetic audiences, persona simulation, or LLM-based human behavior modeling.
• Familiarity with the synthetic audiences research literature (e.g., silicon samples, generative agents, SubPOP, HumanLM, DeepBind).
• Experience with public opinion or survey data (GSS, ANES, WVS, MIDUS) or panel-based consumer research data.

Responsibilities

• Design, build, and ship LLM-powered systems that simulate consumer audiences end-to-end, from proof-of-concept to production.
• Develop complex inference and reasoning harnesses on top of frontier LLMs, agentic flows, persona conditioning, retrieval, and sampling strategies tuned for distributional fidelity.
• Fine-tune LLMs on survey, panel, and behavioral data to improve alignment with real-world audience distributions; own the full loop from data curation through eval.
• Build the evaluation datasets, benchmarks, and harnesses that define what "good" means for synthetic audience quality - distributional fidelity, behavioral validity, subgroup calibration.
• Partner with product management, applied science, and engineering to translate a fast-moving research literature into shipping product features.

Benefits

• comprehensive benefits programs