Senior Machine Learning Engineer
About this role
Developing the underlying infrastructure for language models and AI systems takes center stage in daily tasks, ensuring smooth transitions from experimentation to production. This engineering-heavy role focuses on building scalable platforms that allow researchers and AI engineers to rapidly train, fine-tune, evaluate, and monitor models while maintaining strict reproducibility and reliability.
Air maintains an AI-native platform for government agencies and industrial suppliers, requiring robust systems for dataset pipelines, model serving, and production observability. An ideal candidate brings strong distributed systems experience and a deep understanding of the modern LLM lifecycle to support mission-critical operations.
What you will do
- Design and build LLMOps infrastructure supporting the development, evaluation, deployment, and continuous improvement of production language models.
- Develop scalable training, fine-tuning, and distributed inference systems for commercial and open-weight models.
- Build data pipelines for training, fine-tuning, evaluation, dataset versioning, lineage, and synthetic data generation.
- Create experiment management infrastructure that enables engineers to compare models, datasets, hyperparameters, and prompts.
- Build automated evaluation pipelines, model registries, artifact management, and promotion workflows across environments.
- Develop observability tools for model training and inference covering metrics, tracing, logging, resource utilization, latency, and cost.
- Investigate model and infrastructure failures across data pipelines, training jobs, inference services, and distributed systems.
What you need
- Five or more years of experience building production machine learning systems, ML infrastructure, or distributed systems.
- Deep experience designing, building, and operating production ML infrastructure and platforms for large language models.
- Practical knowledge of LLM fine-tuning and post-training workflows including supervised fine-tuning and parameter-efficient approaches.
- Strong programming experience in Python alongside the ability to build production-quality software.
- Hands-on background with containers, Kubernetes, and cloud platforms or dedicated GPU providers.
- Strong understanding of distributed systems and the challenges of running computationally intensive ML workloads at scale.
- U.S. Citizenship is required.
Nice to have
- Current possession of a U.S. security clearance, or the ability to obtain one with company sponsorship
- Experience in or exposure to the nuances of a startup or entrepreneurial environment
- Experience building secure code execution environments or sandboxes for AI agents
- Experience with multi-agent architectures, agent-to-agent communication, or distributed agent execution
- Experience working in government, defense, or other mission-critical environments
Skills and tech stack
Questions candidates ask
What is the remote work policy for this position?
The role is based in the Pittsburgh, PA office or open to remote opportunities. It may require up to 25% travel, including periodic visits to the Pittsburgh, PA and Arlington, VA offices for team collaboration, planning activities, and in-person meetings.
Are there citizenship or security clearance requirements?
U.S. Citizenship is required for this position. Candidates may also have the opportunity to obtain a U.S. security clearance with company sponsorship if they do not currently possess one.
What kind of systems and infrastructure will I be building?
You will build platforms and infrastructure that allow AI engineers and researchers to rapidly experiment with models, datasets, and training techniques. This includes LLMOps, fine-tuning infrastructure, model evaluation, dataset pipelines, experiment management, model serving, and production observability.