Senior Machine Learning Engineer

Summary

Join Atomi's AI/ML team and lead the development of ML systems for student performance modeling and personalized recommendations. You will develop sophisticated feature engineering pipelines, deep learning models, and optimize inference performance. Responsibilities include leading experimental design, validating hypotheses, and developing testing frameworks. Collaboration with MLOps engineers, software engineers, and product designers is crucial. The ideal candidate possesses expertise in deep learning, data engineering, and statistical analysis, along with proficiency in Python and relevant ML frameworks. Atomi offers tailored flexibility, generous leave, location independence, growth opportunities, and holistic wellbeing benefits.

Requirements

• Expertise in deep learning (DL) techniques with proven experience building, optimising and deploying neural network architectures
• Expertise in data/feature engineering of interaction data for sequence modelling
• Strong analytical skills with experience in statistical analysis, online controlled experiments and data visualisation
• Proficient in Python programming and version control systems (Git)
• Hands-on experience using common DL frameworks (e.g. PyTorch, NVIDIA Merlin), ML orchestration tools (e.g. Metaflow) and relevant cloud MLOps services (e.g. AWS SageMaker)
• Proficient in SQL and data manipulation techniques
• All applicants must hold Australian working rights

Responsibilities

• Develop sophisticated feature engineering pipelines for temporal student-content interaction data
• Develop deep learning models for student performance prediction and personalised content recommendation whilst handling data sparsity, conducting thorough experimentation to improve accuracy while documenting methodologies and results
• Optimise inference performance through compression techniques
• Contribute to developing and maintaining scalable ELT pipelines generating high quality data for ML workflows
• Lead experimental design and implementation
• Help design and execute online tests to validate hypotheses on ML capabilities
• Develop testing frameworks for streamlining hypothesis driven ML development
• Write production-ready, testable code, participate in code reviews, build reusable libraries and contribute to the development of data, feature and ML services
• Collaborate with MLOps engineers and partner with software engineers and product designers/managers to ensure that the ML solutions are user-centric and reliably integrated in production

Preferred Qualifications

• Bonus for production experience with representation learning, sequence modelling (transformers, LSTMs), approaches to handle data sparsity issues, and personalised recommendation systems
• Bonus for familiarity with causal inference and bandit experiments
• Bonus for proficiency with modern data processing frameworks (e.g., Apache Spark, Beam, NVIDIA Merlin NVTabular) and experience with feature stores (e.g. Feast or SageMaker Feature Store) for managing ML features at scale

Benefits

• Tailored flexibility : Enjoy work-life balance with hours that adapt to your needs, whether for university, family or personal time
• Generous leave : Experience unlimited paid leave options as a permanent team member
• Location Independence : Work from Sydney, interstate or wherever you find inspiration
• Growth and development : Use ongoing opportunities to improve your skills and expand your knowledge
• Holistic wellbeing : Benefit from a comprehensive employee assistance program subscription and additional wellbeing leave, supporting your best self
• Parental support: Enjoy 14 weeks of paid leave for new parents, with additional leave specifically for birthing parents
• Regular social in-person and remote events: Including team sports competitions, trivia nights and themed annual events
• Atomi access : Enjoy free Atomi services for you and your family
• Onsite teams : Our Sydney office is ideally located in a central setting with cafes, shops and public transport nearby