Senior Applied Scientist

Zillow

. Own LLM post-training pipelines, including SFT, DPO, and reinforcement fine-tuning (RFT/GRPO), running training end-to-end on GPU infrastructure .

Posted 9/17/2026full-timeRemote • United StatesSenior💰 $152,900 - $257,100 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in LLM post-training pipelines, including SFT, DPO, and reinforcement fine-tuning, with a strong foundation in generative AI and reward model development. Capable of providing technical leadership and mentorship while driving complex projects in collaboration with cross-functional teams.

Highest-signal resume keywords

LLM Post-Training PipelinesReinforcement Learning Fine-TuningGenerative AI KnowledgePython ProgrammingML Frameworks (PyTorch, TensorFlow)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SFTDPORFTGRPOReward Model DevelopmentEnd-to-End TrainingMulti-Turn Trace AnalysisLLM-as-a-JudgePreference LearningGPU Infrastructure

Soft Skills

Technical LeadershipMentorshipProblem SolvingCollaboration

Tools & Technologies

DatabricksFireworks

Certifications & Qualifications

Master's Degree in Computer SciencePhD in Computer Science or Machine Learning

Industry Keywords

Generative AIFoundation ModelsTransformersReinforcement LearningPreference Learning

Tech Stack

Tools & technologies

PythonPyTorchTensorflow

About the role

Key responsibilities & impact

Own LLM post-training pipelines, including SFT, DPO, and reinforcement fine-tuning (RFT/GRPO), running training end-to-end on GPU infrastructure
Build and train fast, multi-category reward models that emit scalar signals from contrastive preference pairs
Design on-policy and online assessment by scoring live agent trajectories using LLM-as-a-Judge frameworks
Translate offline evaluation rubrics into generalizable reward functions for arbitrary production traces
Drive complex, ambiguous work end-to-end in collaboration with product, engineering, science, platform, and data teams
Provide technical leadership and mentorship
Translate state-of-the-art AI research into production impact

Requirements

What you’ll need

Master's degree or higher in Computer Science or a related field
Hands-on experience with LLM post-training and RL fine-tuning: SFT, DPO, RFT/GRPO, running end-to-end training runs on GPU infrastructure
Exposure to reward model development; research-level experience is sufficient
Strong knowledge of generative AI, including foundation models, transformers, reinforcement learning, and preference learning
Ability to independently scope and solve ambiguous problems end-to-end while providing technical leadership to scientists and MLEs
Strong programming skills, especially Python
Experience with ML frameworks such as PyTorch or TensorFlow
PhD in Computer Science, Machine Learning, or a related field (nice-to-have)
Experience evaluating agentic AI: multi-turn trace analysis, LLM-as-a-Judge, and the interaction between offline evals and online monitoring (nice-to-have)
Published work in post-training, RLHF/RLAIF, preference learning, or reward modeling (nice-to-have)
Experience with GPU training platforms such as Databricks or Fireworks (nice-to-have)

Benefits

Comp & perks

Equity awards based on factors such as experience, performance and location
Remote work from a physical location of choice
Flexibility to work from wherever most productive
Accommodation support for disabilities or special needs