Member of Technical Staff, Model Evaluation

Inception Labs; Palo Alto (CA); Hybrid; USD 150,000 - 185,000; Full time

Job summary

Inception Labs in Palo Alto seeks experienced engineers and scientists to develop evaluation metrics 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.

Join a cross-disciplinary team shaping rigorous testing, metric definitions, and automated pipelines that integrate with model training and deployment.

Qualifications

• BS/MS/PhD in Computer Science, Machine Learning, Statistics, or a related field (or equivalent experience).
• 2+ years of experience in ML evaluation, applied ML research, or related engineering role.
• Strong 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.
• Solid foundation in statistics, experimental design, and hypothesis testing.
• Experience with version control (Git) and containerization (Docker).
• Excellent communication skills with the ability to distill complex evaluation results into actionable insights.

Responsibilities

• Design, develop, and maintain robust evaluation frameworks and benchmarks for measuring LLM performance across diverse tasks and domains.
• Define and implement quantitative metrics that capture model quality, safety, reliability, and regression detection.
• Build scalable, automated evaluation pipelines that integrate into model training and deployment workflows.
• 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.

Skills

Python

PyTorch

Evaluation metrics

Statistics

Experiment design

Communication

Git

Docker

Education

BS/MS/PhD in Computer Science or related field

Tools

Git

Docker

Job description

The Role

We seek experienced engineers and scientists to develop the evaluation metrics 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

• Design, develop, and maintain robust evaluation frameworks and benchmarks for measuring LLM performance across diverse tasks and domains.
• Define and implement quantitative metrics that capture model quality, safety, reliability, and regression detection.
• Build scalable, automated evaluation pipelines that integrate into model training and deployment workflows.
• 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.

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.
• Strong 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.
• Solid foundation in statistics, experimental design, and hypothesis testing.
• Experience with version control (Git) and containerization (Docker).
• Excellent communication skills with the ability to distill complex evaluation results into actionable insights.

Preferred Skills

• Experience with human-in-the-loop evaluation systems (Likert-scale annotation, pairwise preference ranking, red-teaming).
• Familiarity with LLM safety and alignment evaluation (toxicity, hallucination detection, factual grounding).
• Knowledge of existing benchmark suites (MMLU, HumanEval, HELM, BIG-Bench) and their limitations.
• Experience building evaluation infrastructure at scale using cloud platforms (AWS, GCP, Azure).
• Familiarity with MLOps practices and CI/CD pipelines for model validation.
• Experience with data engineering, large-scale data labeling, or synthetic data generation for evaluation purposes.