Applied Research Engineer
San Francisconot specifiedPosted 7mo ago
About the role
Design, train, and deploy models that power real business workflows, working directly with customer data, constraints, and feedback to build production systems.
Responsibilities
- Train and adapt models: Fine-tune and post-train models on customer-specific data using RLQF and related techniques.
- Close the loop: Turn real user interactions, corrections, and workflows into training signals.
- Own end-to-end systems: Build from data ingestion and curation through training, evaluation, and deployment.
- Evaluate in production: Design evaluation frameworks that reflect real-world performance.
- Work with customers: Partner directly with users to understand workflows and translate them into model behavior.
- Ship and iterate: Continuously improve models based on live feedback and measurable outcomes.
Qualifications
- Strong hands-on experience training, fine-tuning, or post-training ML models.
- Experience working with messy, real-world data.
- Familiarity with reinforcement learning, feedback-driven training such as RLHF or RLAIF, or evaluation systems.
- Ability to move quickly from problem to data to model to iteration.
- Strong engineering instincts and comfort owning systems end-to-end.
- Bias toward shipping and improving systems, not just running experiments.
Skills
Required:ML model training and fine-tuningHandling real-world messy dataReinforcement learning techniquesSystem engineering and deployment
Last updated Mar 25, 2026. Interested? Apply on the company site.
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