Base Salary
$154k - $217k/yr
Responsibilities
- Design, build, and operate scalable infrastructure for evaluations across large model fleets, datasets, modalities, and compute environments.
- Develop orchestration, scheduling, data, and artifact-management systems for reliable and reproducible evaluation workflows.
- Build APIs, abstractions, and developer tools that help researchers launch, debug, compare, and interpret evaluations.
- Improve reliability through testing, observability, capacity planning, performance optimization, and automated failure recovery.
- Partner with research and engineering teams to translate evaluation requirements into reusable platform capabilities.
Requirements
- Bachelor’s degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
- At least 3 years of software engineering experience building backend, distributed, data, or machine learning infrastructure.
- Proficiency in Python, C++, or another systems programming language.
- Experience designing, implementing, and operating reliable services, platforms, or data-processing systems.
- Experience independently delivering medium- to large-scale technical projects from design through production operation.
- Knowledge of testing, code review, observability, incident response, and performance analysis.
- Ability to work effectively with researchers and engineers and adapt to changing requirements.
- Preferred: experience with large-scale ML training, inference, evaluation, data processing, distributed compute, workflow orchestration, containers, cluster schedulers, cloud infrastructure, performance profiling, reliability engineering, production observability, language-model post-training workflows, internal platforms, or developer tools.
Benefits
- Base salary of $154,003/year to $217,000/year, plus bonus, equity, and benefits.
Categories
About Meta
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