Senior Machine Learning Engineer at Air

Department: AI

Setup: Remote
Location: Virginia · Pittsburgh, Pennsylvania
Type: Full-time
Level: senior
Posted: Sep 8, 2026

Description

Air is hiring a Senior Machine Learning Engineer to build and operate the infrastructure powering the development, evaluation, deployment, monitoring, and continuous improvement of language models and AI systems. The role covers LLMOps, fine-tuning and post-training infrastructure, distributed GPU training, data and experiment pipelines, model registries, serving, observability, deployment automation, and production reliability. It is a full-time position based in the Pittsburgh, Pennsylvania office or open to remote opportunities, with up to 25% travel to Pittsburgh and Arlington, Virginia.

Required Skills

LLMOps
Fine-tuning
Distributed training
Python
Kubernetes
AWS
Observability
Model serving

Additional Information

Required Previous Experiences: 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 supervised fine-tuning, LoRA/QLoRA, and preference optimization
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
Strong understanding of distributed systems and running computationally intensive ML workloads at scale
Strong programming experience in Python and 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
Preferred Qualifications: Current U.S. security clearance or ability to obtain one with sponsorship
Experience in or exposure to 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 with fine-tuning, post-training, reinforcement learning, or synthetic data generation
Experience building AI observability, tracing, and debugging infrastructure
Experience optimizing inference latency, throughput, GPU utilization, or model-serving costs
Experience with AI security, adversarial testing, or securing agentic systems
Experience working in government, defense, or other mission-critical environments
Translation Company Name: Air