Staff ML Engineer (AI)

TL;DR

Staff ML Engineer (AI/ML): Building and scaling predictive test selection models and infrastructure to optimize CI pipelines with an accent on ML architecture, data pipelines, and production scaling. Focus on designing generalized test selection models that reduce feedback loops for engineering teams.

Location: Remote in the ANZ & PST Regions

Company

hirify.global provides a CI platform trusted by leading engineering teams to ship software to over a billion daily users.

What you will do

• Define and lead the ML strategy for predictive test selection, from experimentation to production at scale.
• Design end-to-end ML architecture, including feature engineering, model training, serving infrastructure, and feedback loops.
• Build and maintain scalable data pipelines connecting code change signals with test execution history.
• Implement production observability for models, monitoring accuracy, latency, coverage, and drift.
• Mentor engineers and drive technical best practices through documentation, pairing, and cross-team tooling.
• Collaborate with customers to ensure test selection delivers real impact within their development workflows.

Requirements

• Deep proficiency in Python and SQL, with experience building production ML systems end-to-end.
• Proven experience designing and operating ML infrastructure at scale (model registries, feature stores, serving layers).
• Strong experience with large-scale data processing using batch or streaming frameworks like Spark or Flink.
• Comfort working with AWS and containerized workloads (Docker, Kubernetes).
• Hands-on experience with classification, ranking, or prediction problems with challenging signal-to-noise ratios.
• Must be based in the ANZ or PST regions.

Nice to have

• Experience with code analysis or static analysis tools.
• Familiarity with CI/CD systems, developer tooling, or test infrastructure.
• Experience with Ruby on Rails, React, GraphQL, or Go.
• Background in search ranking, recommendation systems, or predicting relevance from sparse signals.

Culture & Benefits

• Flexible, remote-first culture designed for autonomy and collaboration.
• Competitive compensation package including salary and equity.
• Meaningful technical challenges involving high-scale data and ML infrastructure.
• Opportunities for professional growth, technical leadership, and cross-team influence.
• Inclusive environment that values diversity and provides reasonable accommodations.