Research Engineer, Domain Scaling
Own end-to-end data strategy and RL environment creation for domain-specific knowledge work (finance, healthcare, legal). Combine applied research with hands-on data sourcing, vendor management, and model performance measurement.
About the job
Responsibilities
- Own the data strategy for knowledge work verticals end-to-end, from task sourcing through RL training
- Manage technical relationships with external data vendors, including evaluation of data quality and reward design
- Collaborate with domain experts to design data pipelines and evaluations
- Explore novel ways of creating RL envs for high value tasks
- Develop and improve QA frameworks to catch reward hacking and ensure env quality
- Run generalization experiments to measure how data strategy changes improve model capabilities
- Partner with other RL research teams and product teams to translate capability goals into training envs and evals
Requirements
- Experience with fine-tuning large language models for specific domains or real-world use cases
- Experience with reinforcement learning, reward design, or training data curation for LLMs
- Comfortable managing technical vendor relationships and iterating quickly on feedback
- Value in reading through datasets to understand them and spot issues
- Strong cross-functional collaboration skills
- Passionate about making AI more useful and accessible across different industries
- Excited about a role that includes a combination of applied research and hands-on data work
Nice-to-Haves
- Experience training production ML systems
- Experience designing evals or benchmarks for LLMs
- Domain expertise in a vertical where models would be more useful
- Experience working with external vendors or technical partners
Education
- Bachelor’s degree or an equivalent combination of education, training, and/or experience in a field relevant to the role
Compensation
- Annual Salary: $1—$2 USD
Skills
Reinforcement Learning, Fine-Tuning Llms, Reward Design, Training Data Curation, Data Pipelines, Qa Frameworks, Model Evaluation, Vendor Management, Cross-Functional Collaboration, Applied Research
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