Software Engineer (ML Infrastructure)

Location: Bellevue, Los Angeles, or Palo Alto, United States; office work is expected 4+ days per week.

Base salary: $178,000–$313,000 annually, depending on the assigned U.S. pay zone; eligible for RSUs.

Company

hirify.global is a technology company operating hirify.globalchat and other digital products that use camera, communication, and augmented computing technologies.

What you will do

• Design and optimize infrastructure systems for large-scale machine learning workloads.
• Build feature generation and serving pipelines for online inference and offline training data.
• Develop high-performance model training, evaluation, and inference systems in the cloud.
• Build data management systems for scalable collection, labeling, processing, and evaluation.
• Deploy production machine learning models in collaboration with ML engineers.
• Ship scalable, secure, reliable services using AI tools and high-velocity engineering workflows.

Requirements

• 6+ years of post-Bachelor’s software development experience, or equivalent advanced-degree experience.
• Bachelor’s degree in a technical field or equivalent experience.
• Strong programming skills in Python and Java.
• Understanding of distributed systems and large-scale ML infrastructure.
• Experience with large-scale production machine learning systems, distributed systems, or big data processing.
• Experience operating highly available systems at significant scale.

Nice to have

• Experience with ML training platforms or AI model inference optimization.
• Familiarity with TensorFlow, PyTorch, Caffe2, Spark ML, scikit-learn, or related frameworks.
• Master’s or PhD in a technical field such as computer science.
• Experience with Spark, Flink, or Ray.

Culture & Benefits

• Default-together working model with in-office collaboration expected 4+ days per week.
• Paid parental leave and comprehensive medical coverage.
• Emotional and mental health support programs.
• Equity compensation through RSUs.
• Equal opportunity employment and an emphasis on privacy in product development.