Senior Software Engineer, ML Enablement (Machine Learning)

Location: Hybrid role based within commutable distance of hirify.global's Brooklyn office, with office attendance once or twice per week. Remote candidates outside commuting distance may be considered case by case, with occasional travel to the office expected. U.S. geographic pay zones apply.

Salary: $182,000–$246,000 annual in Geo-1; $164,000–$222,000 annual in Geo-2.

Company

hirify.global is a global marketplace for unique and creative goods, building tools and technologies that connect millions of entrepreneurs with buyers.

What you will do

• Build, operate, and improve infrastructure for training and serving machine learning models on Google Cloud and Kubernetes.
• Design and maintain serving infrastructure for internally hosted and open-source LLMs, including model 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 knowledge to infrastructure and system design decisions.
• Partner with Applied Scientists and Engineers to translate model training and serving needs into self-service infrastructure.
• Design large-scale 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 code.
• Hands-on experience with cloud infrastructure, preferably Google Cloud, and Kubernetes, including deploying and operating production workloads.
• Familiarity with observability tooling such as metrics, logging, and tracing for monitoring and debugging distributed systems.
• Working knowledge of machine learning fundamentals, including neural-network deep learning, prediction and inference systems, and LLM serving infrastructure.
• Basic understanding of transformer architectures, predictors, and how model design choices affect serving infrastructure.
• Comfort with system design for large-scale, high-availability infrastructure.

Nice to have

• Additional programming languages beyond Python.

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.
• Equity package and annual performance bonus.
• Competitive benefits supporting employees and their families.
• Development opportunities and support for career growth.
• Visa sponsorship may be available, with opportunities limited to certain roles and skills.

Hiring process

• Submit a resume; a cover letter is optional.
• Reasonable accommodations are available during the interview and hiring process.