Applied AI Training Engineer

Description We are looking for an Applied AI Training Engineer to join a Contract opportunity in San Francisco, California. This role focuses on shaping high-quality data and evaluation assets that strengthen how large language models learn specialized enterprise software concepts, workflows, and decision patterns. The ideal candidate will combine AI training expertise, strong technical judgment, and close collaboration with subject matter experts to improve model accuracy, consistency, and real-world usefulness across complex business scenarios.

Responsibilities:

• Develop structured training examples that help large language models learn enterprise software behaviors, terminology, and task flows with precision.

• Collaborate with subject matter experts to convert product knowledge, process logic, and business context into usable datasets for model training.

• Define data design approaches that expand domain coverage and support reliable model performance across a wide range of user scenarios.

• Build, organize, and enhance large-scale datasets, including generating synthetic examples when they add value to model learning.

• Review model responses, investigate failure patterns, and adjust training content to close knowledge gaps and improve output quality.

• Establish reusable frameworks, taxonomies, and teaching methods for introducing new concepts, schemas, and specialized domains to AI systems.

• Create benchmark datasets and evaluation methods to measure reasoning quality, factual accuracy, coverage, and generalization.

• Implement quality controls that detect inconsistency, ambiguity, imbalance, and missing information within training data collections.

• Write and maintain Python-based tools to create, transform, validate, and manage training data efficiently at scale.

• Refine model behavior to better align responses with customer expectations, domain expertise, and intended software functionality. Requirements • Demonstrated experience designing datasets for training or fine-tuning large language models.

• Strong background in teaching AI systems new domains, workflows, products, or structured knowledge through carefully crafted examples.

• Ability to determine learning objectives and create data that guides model behavior, rather than only annotating existing content.

• Deep understanding of how dataset quality, diversity, structure, and coverage influence model outcomes.

• Hands-on experience with instruction-tuning data, supervised fine-tuning datasets, synthetic data generation, and evaluation design.

• Proficiency in Python for dataset preparation, transformation, validation, and large-scale data management.

• Proven analytical ability with strong attention to detail and sound judgment when assessing data quality and model performance.

• Experience working with enterprise software platforms such as ServiceNow, Oracle, Workday, or other complex SaaS environments is highly valued.

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