Principal Applied Scientist (User Modeling)
J
JobgetherArtificial intelligence
Based in the United States; Remote position for eligible employees based in the United StatesFull-TimePrincipal
Salary$195,600–$312,400 in California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington State, and Washington, D.C.; $185,800–$296,800 in Colorado, Hawaii, Illinois, Maine, Minnesota, Nevada, Ohio, Rhode Island, Vermont, and Virginia.
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Job Details
- Required Skills
- PythonPyTorchTensorflow
Requirements
- PhD in Computer Science, Statistics, Economics, Machine Learning, or a related quantitative field.
- Strong expertise in reinforcement learning or causal inference.
- Hands-on experience modeling user behavior at scale, including sequence models built on event streams, sessions, or trajectories.
- Experience predicting user intent or long-term outcomes.
- Practical expertise in causal inference methods such as propensity scoring, treatment-effect estimation, and off-policy evaluation, or reinforcement learning applied to real-world sequential decision problems.
- Experience with LLM post-training methodologies, reward modeling, preference optimization, or evaluation frameworks for large language models and agentic systems.
- Working proficiency in Python and machine learning frameworks such as PyTorch or TensorFlow.
- Ability to run experiments independently and read, review, debug, and improve code.
- Ability to scope and solve ambiguous technical problems, design rigorous experiments, and translate research findings into practical solutions.
- Ability to guide scientific or engineering initiatives, influence technical direction, and support the development of other technical professionals.
- Knowledge of economics or econometrics, including demand modeling and consumer choice analysis, is preferred.
- Research contributions in reinforcement learning, causal inference, survival analysis, user modeling, or simulation are relevant.
Responsibilities
- Develop user world models that infer user intentions, anticipate future needs, and predict the long-term consequences of actions.
- Own end-to-end LLM post-training pipelines, including SFT, DPO, and RFT/GRPO, using GPU infrastructure.
- Develop and train multi-category process reward models using contrastive preference pairs.
- Design on-policy and online assessment frameworks for live agent trajectories, including LLM-as-a-Judge methods.
- Translate offline evaluation rubrics into generalizable reward functions for production traces.
- Apply sequential decision modeling, treatment-effect estimation, propensity scoring, and off-policy evaluation.
- Define research problems, develop experiments, analyze results, and deliver solutions through production implementation.
- Collaborate with science, engineering, product, platform, and data teams to integrate research capabilities into production.
- Mentor scientists and engineers, influence technical decisions, and communicate research findings.
- Monitor research advances and translate promising techniques into improvements in AI-powered consumer experiences.
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