Staff Machine Learning Software Engineer (Robotics)

Location: Sunnyvale, California, USA; hybrid

Salary: $311,000–$419,000 per year, plus a competitive equity package.

Company

hirify.global develops embodied AI software and foundation models that enable vehicles and robots to perceive, understand, and navigate complex environments.

What you will do

• Design, build, and maintain scalable ML pipelines for data ingestion, model training, evaluation, and research workflows.
• Develop software systems and reusable interfaces connecting data, models, training, evaluation, and robotics workflows.
• Build distributed training and data-processing pipelines for large models and multimodal datasets.
• Own the MEGA codebase, improving architecture, testing, reliability, maintainability, and engineering standards.
• Identify and resolve performance, reliability, and usability bottlenecks across ML workflows.
• Work directly with researchers and engineers to translate new model and experiment requirements into practical software systems.

Requirements

• Strong software engineering skills and experience building high-quality, maintainable software.
• Experience building and maintaining machine learning pipelines or infrastructure for data ingestion, training, evaluation, or experimentation.
• Experience designing reliable, reusable software systems and abstractions that can evolve with changing requirements.
• Hands-on experience with modern machine learning frameworks, large datasets, large models, or computationally demanding ML workloads.
• Strong debugging skills, software testing experience, and knowledge of code-quality practices for shared codebases.
• Ability to collaborate with researchers and engineers and translate research requirements into practical systems.

Nice to have

• Experience with distributed training, multi-node systems, or large-scale data processing.
• Experience supporting foundation-model training, multimodal models, video models, or vision-language models.
• Experience with robotics, embodied AI, simulation, or robot-interaction data.
• Experience building infrastructure in a fast-moving applied research environment.
• Experience improving the performance, reliability, or developer experience of ML training systems.

Culture & Benefits

• Hybrid working policy combining time in offices and workshops with working from home.
• Core working hours with flexibility to determine a schedule that works for the team.
• Competitive equity package.
• Inclusive and collaborative environment focused on learning, innovation, and complex technical challenges.

Hiring process

• Inclusive interview experience with accommodations available upon request.