Staff Software Engineer, ML Training Infrastructure (AI)

Location: Pittsburgh, Pennsylvania, or remote within the United States. The position may require verification of residence, U.S. person status, and/or citizenship status and may involve U.S. export-controlled technologies.

Company

hirify.global develops AI-powered autonomous systems combining robotics, machine learning, and cloud technologies for the trucking industry.

What you will do

• Design and evolve high-performance ML training platform components, including orchestration, training abstractions, control-plane services, observability, and performance tuning.
• Deliver end-to-end ML pipelines covering log processing, feature extraction, dataset schemas and storage, model configuration, training, profiling, and acceleration.
• Analyze infrastructure performance and resolve bottlenecks in large-scale training workflows.
• Develop abstractions, frameworks, and tooling that help machine learning engineers iterate rapidly and work independently.
• Collaborate across ML Platform, Infrastructure, Autonomy, and Safety Evaluation teams to accelerate autonomous vehicle development.
• Set and promote a high standard of engineering excellence across the team.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 6+ years of experience with ML platforms and ML-based applications.
• Strong programming skills in Python, C++, or equivalent technologies.
• Experience with Lance, PyTorch, Ray Data, or equivalent tools.
• Experience building scalable, reliable infrastructure and large-scale data processing or model-training pipelines.
• Strong analytical, problem-solving, communication, and cross-functional alignment skills.

Nice to have

• Experience with autonomous vehicles.
• Experience with modeling, model optimization, or profiling and acceleration workflows.

Culture & Benefits

• Work on autonomous technology designed to improve safety, reliability, and operational efficiency.
• Collaborate with teams developing real-world systems for demanding trucking environments.
• Contribute to an inclusive culture focused on entrepreneurship, innovation, and engineering excellence.
• Equal-opportunity workplace committed to diverse teams.