We are seeking an experienced Senior Machine Learning Engineer to join our AI/ML team and build the infrastructure that powers the development, evaluation, deployment, and continuous improvement of our language models and AI systems.

As our AI capabilities expand, we need robust infrastructure for moving models from experimentation into production. This role will own critical parts of that lifecycle, including LLMOps, fine-tuning infrastructure, model evaluation, dataset pipelines, experiment management, model serving, and production observability.

In order to do this job well: This is an engineering-heavy ML role. You will build platforms and infrastructure that allow AI engineers and researchers to rapidly experiment with models, datasets, and training techniques while maintaining the reproducibility, scalability, and reliability required for production systems.

You will work across the full model lifecycle - from dataset creation and experimentation through training, evaluation, deployment, monitoring, and iteration.

This role is a full-time position based in our Pittsburgh, PA office or open to Remote Opportunities.

This role may require up to 25% travel, including periodic travel to our Pittsburgh, PA and Arlington, VA offices for team collaboration, planning activities, and in-person meetings.

• 5+ years of experience building production machine learning systems, ML infrastructure, distributed systems, or similar technical systems.
• Deep experience designing, building, and operating production ML infrastructure or ML platforms.
• Experience building infrastructure for training, fine-tuning, evaluating, deploying, and monitoring large language models or other large-scale deep learning models.
• Experience with LLM fine-tuning and post-training workflows, including techniques such as supervised fine-tuning, LoRA/QLoRA or other parameter-efficient approaches, and preference optimization.
• Strong understanding of the modern LLM lifecycle, including data preparation, training, evaluation, model artifacts, deployment, inference, monitoring, and iteration.
• Experience building reproducible ML pipelines involving dataset versioning, experiment tracking, model versioning, and automated evaluation.
• Experience building and operating production GPU infrastructure across AWS, GCP, Azure, or dedicated GPU providers, including training and/or inference workloads.
• Strong understanding of distributed systems and the challenges involved in running computationally intensive ML workloads at scale.
• Strong programming experience in Python and experience building production-quality software.
• Deep experience with containers, Kubernetes, and cloud platforms such as AWS, GCP, or Azure.
• Experience designing scalable APIs, services, asynchronous workloads, and data-processing pipelines.
• Strong understanding of observability and operational reliability for production ML systems.
• Comfortable debugging failures across training code, datasets, models, GPUs, distributed systems, and cloud infrastructure.
• Able to move between ML experimentation and infrastructure engineering, understanding the needs of researchers and AI engineers while building systems that make those workflows scalable and reproducible.
• Comfortable working in a rapidly evolving field where tooling, models, and best practices change quickly.