Senior Machine Learning Engineer (ML Training Infrastructure)

Location: Hybrid in Sunnyvale, California; expected to report to the specific location at least 3 times a week. Willingness to travel to Sunnyvale, CA as needed; travel is required for less than 25% of the role.

Salary: $170,000–$240,800 per year. Bonus potential and benefits are also available.

Company

hirify.global develops vehicles and technologies focused on zero crashes, zero emissions, and zero congestion, including intelligent driving solutions.

What you will do

• Design and develop scalable, reliable, high-performance ML frameworks for model training at scale.
• Improve system observability, debuggability, operational excellence, and user experience.
• Analyze and optimize model training performance across distributed workflows and heterogeneous hardware environments.
• Maximize resource utilization and reduce infrastructure costs.
• Collaborate with machine learning engineers, research scientists, and cross-functional partners to integrate new platform features and technologies.

Requirements

• Bachelor's degree or higher in Computer Science or an equivalent major, or equivalent relevant experience.
• 5+ years of professional software engineering experience.
• At least 1 year of specialized experience in AI/ML infrastructure, including enabling distributed training for scaling large ML models.
• Strong Python programming skills and proficiency with PyTorch, TensorFlow, or a similar framework.
• Experience with distributed computing, GPU computing, and cloud environments including AWS, GCP, or Azure.
• Willingness to travel to Sunnyvale, California as needed.
• Comfort working in highly ambiguous and dynamic environments.

Nice to have

• Self-motivation, strong execution, and an impact-oriented approach.
• Extensive experience with PyTorch 2.x or later and distributed training frameworks.
• Experience designing and developing training frameworks supporting FSDP, pipeline parallelism, and other scalable approaches for training large foundational models.
• Experience profiling, analyzing, debugging, and optimizing training and data-loading performance.
• Excellent communication skills for resolving disagreements, building consensus, communicating risks, and providing constructive feedback.

Culture & Benefits

• Hybrid work arrangement with regular on-site attendance.
• Medical, dental, vision, Health Savings Account, and Flexible Spending Account options.
• Retirement savings plan, sickness and accident benefits, life insurance, and paid vacation and holidays.
• Tuition assistance, employee assistance program, and GM vehicle discounts.
• Relocation benefits are available for candidates who qualify under company policy.
• Inclusive workplace focused on belonging, meaningful change, and equitable employment practices.

Hiring process

• Role-related assessments and pre-employment screening may be required where applicable.