Staff Software Engineer (ML Training Infrastructure)

TL;DR

Staff Software Engineer (ML Infrastructure): Building and optimizing scalable ML training frameworks and infrastructure for autonomous trucking systems with an accent on developer experience for ML engineers and model deployment. Focus on designing end-to-end ML pipelines, model optimization, and ensuring high-performance data processing.

Location: Remote (USA) or Hybrid in Pittsburgh, PA. Must be a U.S. person or hold appropriate citizenship/residency status due to U.S. national security and export control laws.

Company

hirify.global is developing autonomous systems and AI to enhance safety and efficiency in the trucking transportation industry.

What you will do

• Build and maintain a reliable, scalable training framework for ML modeling needs.
• Improve the developer experience for ML engineers by creating tools for testing, validation, and model understanding.
• Develop and optimize model deployment and large-scale data processing pipelines.
• Design and implement ML platforms and ML-based applications.
• Collaborate with multiple modeling teams to align on design tradeoffs and infrastructure needs.

Requirements

• Proven experience building end-to-end ML model pipelines, including logs processing, feature extraction, and serving deployment.
• Experience shipping large-scale ML products (NLP, computer vision, or recommender systems) with business impact.
• Ability to create effective abstractions and tooling for rapid ML iteration.
• Experience building scalable and reliable infrastructure in fast-paced environments.
• U.S. person status or citizenship may be required for compliance with national security laws.

Nice to have

• Prior experience in Autonomous Vehicles (AV), perception, or decision-making domains.

Culture & Benefits

• Equal opportunity workplace committed to inclusion, entrepreneurship, and innovation.
• Opportunity to work with cutting-edge AI, robotics, and cloud technologies in a demanding real-world environment.
• Collaborative culture focused on safety, reliability, and efficiency of modern operations.