Scala Senior Software Engineer I (Machine Learning)

Location

Brooklyn, New York, US. Candidates should be within commutable distance of the Brooklyn office and attend once or twice per week depending on proximity. Remote candidates outside commuting distance may be considered case by case, with occasional travel to the office expected.

Salary

Annual base salary: $182,000–$246,000 in Geo-1 or $164,000–$222,000 in Geo-2. Additional compensation includes equity and an annual performance bonus.

Company

hirify.global operates a global marketplace for unique and creative goods, building technology that connects millions of entrepreneurs with millions of buyers.

What you will do

• Build, operate, and improve infrastructure for training and serving machine learning models on GCP and Kubernetes.
• Design and maintain serving infrastructure for internally hosted and open-source LLMs, including deployment, resource management, and performance tuning.
• Develop production-grade Python code, participate in code reviews and pair programming, and contribute to architectural decisions.
• Apply machine learning, deep learning, transformer, prediction, and inference concepts to infrastructure and system design decisions.
• Partner with Applied Scientists to translate model training and serving needs into self-service infrastructure.
• Design systems serving 100M+ users while balancing performance, cost, reliability, and observability.

Requirements

• Bachelor's degree in Computer Science, Applied Statistics, Mathematics, Electrical Engineering, or a related quantitative field, or equivalent professional experience.
• 5+ years of professional experience building, iterating on, and troubleshooting complex backend and infrastructure systems.
• Strong software engineering fundamentals, including algorithms and data structures, with the ability to write production-ready Python.
• Hands-on experience with cloud infrastructure, preferably Google Cloud, and Kubernetes, including deploying and operating production workloads.
• Familiarity with observability tooling including metrics, logging, and tracing for monitoring and debugging distributed systems.
• Working knowledge of machine learning fundamentals and LLM serving infrastructure, including hosting open-source models.
• Basic understanding of transformer architectures, predictors, and how model design choices affect serving infrastructure.
• Comfort with system design for large-scale, high-availability infrastructure.

Culture & Benefits

• Collaborative, low-process platform team working with partner teams across the organization.
• Product-driven approach to machine learning infrastructure and self-service platforms.
• Competitive benefits supporting employees and their families.
• Equity package and annual performance bonus.
• Visa sponsorship is supported, though availability may be limited to certain roles and skills.

Hiring process

• Applicants should submit a resume; a cover letter is optional.
• Workplace accommodations are available during the interview and hiring process.