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Senior Machine Learning Engineer

5 hours, 17 minutes ago
United States
Full-time
Senior
Machine Learning Engineer
Software Development
AWS Azure Deep Learning GCP Kubernetes LLM Machine Learning Python Reinforcement Learning
Apply Now
Air

Air

Air is a Creative Ops System that automates tasks for marketers, streamlines content management, and boosts productivity through image recognition and approval workflows.

Media
11-50
Founded 2017
$30M raised
View All Jobs 3

Description

  • Design and build LLMOps infrastructure for model development, evaluation, deployment, and improvement.
  • Build scalable distributed training, fine-tuning, GPU, and inference infrastructure.
  • Develop data pipelines for training, evaluation, synthetic data generation, dataset versioning, and lineage.
  • Create experiment management, model registry, artifact management, and promotion workflows.
  • Build automated model evaluation, deployment, rollback, canary, and production validation systems.
  • Develop scalable model-serving infrastructure and abstractions supporting multiple models and inference providers.
  • Implement observability for training and inference, including quality, latency, throughput, cost, tracing, and resource metrics.
  • Optimize ML workloads for GPU utilization, performance, reliability, and infrastructure cost.
  • Investigate failures across data pipelines, training jobs, models, distributed systems, and production environments.
  • Partner with AI engineers building agentic systems and evaluate emerging models, frameworks, and training techniques.

Requirements

  • U.S. citizenship is required.
  • 5+ years of experience building production machine learning systems, ML infrastructure, distributed systems, or similar systems.
  • Deep experience designing and operating production ML infrastructure or platforms.
  • Experience with LLM training, fine-tuning, evaluation, deployment, and monitoring.
  • Experience with supervised fine-tuning, LoRA/QLoRA, parameter-efficient fine-tuning, and preference optimization.
  • Experience building reproducible pipelines with dataset, experiment, and model versioning and automated evaluation.
  • Experience operating production GPU infrastructure across AWS, GCP, Azure, or dedicated GPU providers.
  • Strong knowledge of distributed systems, production reliability, and observability for large-scale ML workloads.
  • Strong Python programming and production software engineering experience.
  • Experience with containers, Kubernetes, cloud platforms, scalable APIs, asynchronous workloads, and data pipelines.
  • Ability to debug training code, datasets, models, GPUs, distributed systems, and cloud infrastructure.
  • Current U.S. security clearance or ability to obtain one with sponsorship preferred.
  • Experience with agent architectures, secure execution environments, AI observability, inference optimization, AI security, or government and defense environments preferred.

Benefits

  • Full-time position based in Pittsburgh, PA, or available remotely.
  • Up to 25% travel, including periodic visits to Pittsburgh and Arlington, VA offices.
  • Security clearance sponsorship may be available.
  • Equal opportunity employer.

Interested in this position?

Apply directly on the company website

Apply Now

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