Machine Learning Engineer

Company Description

Air is the leader in Enterprise Readiness. Our mission is to establish readiness as a real-time condition that is continuously achieved. Today, a dangerous Readiness Gap exists between what the front line needs and what is delivered. Our AI-native platform, Air Enterprise Readiness, aligns development, production, delivery, and sustainment into one coordinated execution system for government agencies and industrial suppliers. By revealing true capacity, exposing real constraints, coordinating resources, and executing at the speed of operational demands, the front line gets what it needs to succeed.

Job Description

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.

Scope of Responsibilities

• Design and build LLMOps infrastructure supporting the development, evaluation, deployment, and continuous improvement of production language models.
• Build scalable training and fine-tuning infrastructure for commercial and open-weight language models.
• Develop pipelines supporting supervised fine-tuning, parameter-efficient fine-tuning, preference optimization, and other post-training techniques.
• Build infrastructure for distributed training and GPU-accelerated ML workloads.
• Develop data pipelines for training, fine-tuning, evaluation, and synthetic data generation.
• Build systems for dataset versioning, lineage, quality validation, transformation, and reproducible experimentation.