Senior Machine Learning Engineer (AI)

Location: Remote, with listed engineering locations in Sydney, Melbourne, and Brisbane, Australia. Hiring is possible in any country where hirify.global has a legal entity.

Company

hirify.global develops software products that help teams collaborate and manage different types of work.

What you will do

• Design, build, ship, and operate agentic LLM harnesses for existing and new Rovo AI features.
• Improve quality, reliability, and cost across production LLM systems.
• Build datasets, evaluations, benchmarks, and operational capabilities for machine learning systems at scale.
• Contribute to fine-tuning, model serving, and the long-term technical direction of LLM harness engineering.
• Influence technical direction across engineering and product stakeholders and lead small virtual squads.
• Coach junior engineers and drive ambiguous technical outcomes from definition through operation.

Requirements

• Bachelor’s or Master’s degree in Computer Science or a related field, or equivalent practical experience.
• 4+ years of industry experience in machine learning, including designing, evaluating, and operating LLM-based or agentic systems.
• Strong coding skills in Kotlin and/or Python with experience writing performant, production-quality code.
• Experience operating machine learning systems in production and improving quality, reliability, and cost.
• Experience building datasets, evaluations, and benchmarks at scale; familiarity with SQL, distributed data processing, and cloud data environments.
• Strong communication, technical leadership, ownership, and coaching skills.

Nice to have

• Experience fine-tuning smaller language models.
• Experience serving and operating LLMs with technologies such as NVIDIA Triton Inference Server.
• Experience applying machine learning to personalization or context-aware product experiences.
• Experience solving complex problems in enterprise SaaS or similarly large-scale product environments.

Culture & Benefits

• Remote, office-based, or combined work arrangements are available.
• High degree of engineering ownership and autonomy.
• Asynchronous collaboration and protected focus time.
• Health and wellbeing resources.
• Paid volunteer days and community-oriented benefits.