Job description

The Role We seek experienced engineers and scientists to develop the evaluation infrastructure and systems that drive frontier LLM performance. You'll design the frameworks that tell us whether our models are improving and ensure they perform reliably at scale in production. Key Responsibilities • Build scalable, automated evaluation pipelines that integrate into model training and deployment workflows. • Design, develop, and maintain robust evaluation frameworks and benchmarks for measuring LLM performance across diverse tasks and domains. • Conduct rigorous statistical analysis of model outputs to identify failure modes, biases, and performance gaps. • Partner with product and customer-facing teams to translate real-world use cases into meaningful evaluation criteria. • Define and implement quantitative metrics that capture model quality, safety, reliability, and regression detection. Qualifications • BS/MS/PhD in Computer Science, Machine Learning, Statistics, or a related field (or equivalent experience). • At least 2 years of experience in ML evaluation, applied ML research, or a related engineering role. • Experience with version control (Git), containerization (Docker), and cloud services (AWS, GCP, or Azure). • Understanding of LLM fundamentals (autoregressive generation, instruction tuning, RLHF, in-context learning, decoding strategies). • Proficiency in Python and ML frameworks such as PyTorch. • Experience designing and implementing evaluation metrics and benchmarks for generative models. • Excellent communication skills with the ability to distill complex evaluation results into actionable insights. Preferred Skills • Knowledge of existing benchmark suites and familiarity with agentic evaluations (such as SWE-bench or GPQA/GDPval) and their limitations. • Experience building evaluation infrastructure at scale using cloud platforms (AWS, GCP, Azure) and container orchestration tools (e.g., Kubernetes, Terraform). • Familiarity with MLOps practices and CI/CD pipelines for model validation and infrastructure deployment. • Experience with data engineering, large-scale data labeling, or synthetic data generation for evaluation purposes. • Familiarity with LLM safety and alignment evaluation (toxicity, hallucination detection, factual grounding). • Experience with human-in-the-loop evaluation systems (pairwise preference ranking, red-teaming). •

Requirements

Must-haves

Build scalable, automated evaluation pipelines that integrate into model training and deployment workflows.
Design, develop, and maintain robust evaluation frameworks and benchmarks for measuring LLM performance across diverse tasks and domains.
Conduct rigorous statistical analysis of model outputs to identify failure modes, biases, and performance gaps.
Partner with product and customer-facing teams to translate real-world use cases into meaningful evaluation criteria.
Define and implement quantitative metrics that capture model quality, safety, reliability, and regression detection.
BS/MS/PhD in Computer Science, Machine Learning, Statistics, or a related field (or equivalent experience).
At least 2 years of experience in ML evaluation, applied ML research, or a related engineering role.
Experience with version control (Git), containerization (Docker), and cloud services (AWS, GCP, or Azure).
Understanding of LLM fundamentals (autoregressive generation, instruction tuning, RLHF, in-context learning, decoding strategies).
Proficiency in Python and ML frameworks such as PyTorch.
Experience designing and implementing evaluation metrics and benchmarks for generative models.
Excellent communication skills with the ability to distill complex evaluation results into actionable insights.

Nice-to-haves

Knowledge of existing benchmark suites and familiarity with agentic evaluations (such as SWE-bench or GPQA/GDPval) and their limitations.
Experience building evaluation infrastructure at scale using cloud platforms (AWS, GCP, Azure) and container orchestration tools (e.g., Kubernetes, Terraform).
Familiarity with MLOps practices and CI/CD pipelines for model validation and infrastructure deployment.
Experience with data engineering, large-scale data labeling, or synthetic data generation for evaluation purposes.
Familiarity with LLM safety and alignment evaluation (toxicity, hallucination detection, factual grounding).
Experience with human-in-the-loop evaluation systems (pairwise preference ranking, red-teaming).

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

llmmachine learningmldockerawsgcpazurepythonkubernetesci/cd

Platform: gem
Effort: medium