Scale AI is seeking a technically rigorous and driven AI Research Engineer to join our Enterprise Evaluations team. This high-impact role is critical to our mission of delivering the industry's leading GenAI Evaluation Suite. You will be a hands-on contributor to the core systems that ensure the safety, reliability, and continuous improvement of LLM-powered workflows and agents for the enterprise.
The ideal candidate has a strong foundational knowledge of large language models, a passion for tackling complex evaluation challenges, and thrives in a dynamic, fast-paced research environment. We are looking for an engineer who can think outside the box, stays current with the latest literature in AI evaluation, and is passionate about integrating novel research ideas into our workflows to build best-in-class evaluation systems.
Responsibilities- Partner with Scale’s Operations team and enterprise customers to translate ambiguity into structured evaluation data, guiding the creation and maintenance of gold-standard human-rated datasets and expert rubrics that anchor AI evaluation systems.
- Analyze feedback and collected data to identify patterns, refine evaluation frameworks, and establish iterative improvement loops that enhance the quality and relevance of human-curated assessments.
- Design, research, and develop LLM-as-a-Judge autorater frameworks and AI-assisted evaluation systems. This includes creating models that critique, grade, and explain agent outputs (e.g., RLAIF, model-judging-model setups), along with scalable evaluation pipelines and diagnostic tools.
- Pursue research initiatives that explore new methodologies for automatically analyzing, evaluating, and improving the behavior of enterprise agents, pushing the boundaries of how AI systems are assessed and optimized in real-world contexts.
- Bachelor’s degree in Computer Science, Electrical Engineering, a related field, or equivalent practical experience.
- 2+ years of experience in Machine Learning or Applied Research, focused on applied ML systems or evaluation infrastructure.
- Hands-on experience with Large Language Models (LLMs) and Generative AI in professional or research environments.
- Strong understanding of frontier model evaluation methodologies and the current research landscape.
- Proficiency in Python and major ML frameworks (e.g., PyTorch, TensorFlow).
- Solid engineering and statistical analysis foundation, with experience developing data-driven methods for assessing model quality.
- Advanced degree (Master’s or Ph.D.) in Computer Science, Machine Learning, or a related quantitative field.
- Published research in leading ML or AI conferences such as NeurIPS, ICML, ICLR, or KDD.
- Experience designing, building, or deploying LLM-as-a-Judge frameworks or other automated evaluation systems for complex models.
- Experience collaborating with operations or external teams to define high-quality human annotator guidelines.
- Expertise in ML research engineering, stochastic systems, observability, or LLM-powered applications for model evaluation and analysis.
- Experience contributing to scalable pipelines that automate the evaluation and monitoring of large-scale models and agents.
- Familiarity with distributed computing frameworks and modern cloud infrastructure.
Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.
PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.
About Us:
At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.
We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.
We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at [email protected]. Please see the United States Department of Labor's Know Your Rights poster for additional information.
We comply with the United States Department of Labor's Pay Transparency provision.
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Scale accelerates the development of AI applications by helping machine learning teams generate high-quality ground truth data. Our advanced LiDAR, image, video and NLP annotation APIs allow machine learning teams at companies like OpenAI, Lyft, Pinterest, and Airbnb focus on building differentiated models vs. labeling data.
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