Principal AI Engineer

Job Description

Job Summary 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.

Essential Job Functions and Responsibilities 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 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 Knowledge, Skills, and Abilities 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 Education and Experience 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 Disclaimer The above statements describe the general nature and level of work performed in this role.

Other duties may be assigned. Pay Transparency Statement: 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. * The range displayed does not encompass the full potential of the role, which allows for further growth and career progression.

In addition, as a part of our total compensation package, this role may be eligible for the Vertex Bonus Plan (VOB), a role-specific sales commission/bonus, and/or equity grants. Learn more about Life at Vertex and connect with your recruiter for more details regarding Vertex's compensation and benefit programs. *In no case will your pay fall below applicable local minimum wage requirements.

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