Tag: Preference Tuning & Tool Use
This team will focus on aligning models to human preferences, develop tooling for model tool use, and provide safety controls into model production.
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Senior Machine Learning Engineer
Raleigh, North Carolina
Preference Tuning Team
This machine learning engineer will be focused on developing post-training components of InstructLab, including preference tuning and tool use. Engineers in this role will run experiments, tests, and large-scale distributed jobs in support of post-model training AI product features. They will lead a variety of coding projects in different programming languages (primarily python), helping transition software components from research into product. Engineers in this role will also participate in and lead upstream communities with a focus towards preference tuning and tool use. They will also promote machine learning and data science technologies and ongoing machine learning projects with a variety of technical and non-technical stakeholders.
Note “Apply Now” job descriptions are the official job postings.
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Principal Software Quality Engineer
Boston, Massachusetts
Preference Tuning TeamThis quality engineer will be focused on the quality of post-training components of InstructLab, including preference tuning and tool use. They will be responsible for building a test suite and test automation for preference tuning and tool use, and they will be responsible for evaluating how the quality and performance of models is impacted by changes to the post-training components. Quality engineers who have an interest / familiarity with data science and/or machine learning would be amazing candidates for this role.
Note “Apply Now” job descriptions are the official job postings.