Principal AI Engineer

Role Description

The Principal Engineer, AI Model Training & Data Strategy owns how Commercial AI (CAI) products train, fine-tune, and evaluate models, and how the data behind those models is sourced, curated, stored, and governed. This is primarily a model-training role with a strong secondary focus on the data management and pipelines that make high-quality training possible. The role defines the enterprise training strategy and the standards for how and where training data from Commercial AI products is stored, versioned, and reused.

• Define and own the end-to-end model training strategy across CAI products, spanning traditional AI/ML models and large language models.
• Fine-tune large language models using parameter-efficient techniques (e.g., QLoRA, LoRA, PEFT) and full fine-tuning where warranted.
• Train, evaluate, and tune traditional AI/ML models (classification, regression, ranking, clustering, and similar).
• Work with large volumes of data – design and optimize pipelines for ingestion, cleaning, labeling, and feature engineering.
• Define standards for how and where training data from Commercial AI products is stored, versioned, and accessed (data lakes/warehouses, feature stores, dataset registries).
• Establish data governance, lineage, quality, licensing/consent, and PII-handling practices for training data.
• Build reproducible training pipelines and experiment tracking (datasets, hyperparameters, checkpoints, and metrics).
• Define evaluation methodology and benchmarks for model quality, including offline evaluation and regression testing.
• C curate and clean training, validation, and test datasets, including synthetic data generation where appropriate.
• Optimize training cost and compute utilization (GPU efficiency, distributed training, quantization).
• Partner with product and platform teams to operationalize and hand off trained and fine-tuned models to production.
• Mentor engineers and raise model-training and data-quality maturity across teams.

Qualifications

• Strong hands-on experience training and fine-tuning both traditional AI/ML models and LLMs in production.
• Deep experience with parameter-efficient fine-tuning (QLoRA, LoRA, PEFT), quantization, and the tradeoffs versus full fine-tuning.
• Proficiency with ML/DL frameworks and libraries (e.g., PyTorch, Hugging Face Transformers/PEFT/TRL, scikit-learn).
• Experience building and operating large-scale data pipelines and platforms (e.g., Spark, Ray, dbt, or equivalents).
• Strong grasp of data management: dataset storage architecture, versioning, lineage, governance, and PII handling.
• Experience with experiment tracking and reproducible ML (e.g., MLflow, Weights & Biases).
• Understanding of distributed training and GPU/compute optimization.
• Ability to define strategy and standards while remaining hands-on in code.
• Strong stakeholder collaboration and problem-solving skills.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or related discipline; advanced degree in ML, AI, or Data Science preferred.
• 12 or more years of experience in AI/ML engineering, applied ML, or data engineering, with significant hands-on model training and fine-tuning.

Benefits

• US Base Salary Range: $159,600.00 - $207,500.00.
• Base pay offered to new hires may vary based upon factors including relevant industry and job-related skills and experience, geographic location, and business needs.
• This role may be eligible for the Vertex Bonus Plan (VOB), a role-specific sales commission/bonus, and/or equity grants.