Maya Lin
Machine Learning Engineer specializing in production inference systems, MLOps, and scalable pipelines
Sydney
Professional Summary
Machine Learning Engineer with hands-on experience designing, training, and deploying scalable machine learning systems in production. Proven background in productionising models, building reliable MLOps pipelines, and managing low-latency inference services in cloud environments.
Work Experience
Kestrel Analytics
Sydney, Australia
Machine Learning Engineer
Sept 2022 – Present
- Architected and deployed containerised inference microservices using FastAPI, Triton Inference Server, and Kubernetes, serving 1.2M daily recommendations with sub-45ms p95 latency.
- Implemented automated feature pipelines using Apache Feast and dbt to feed offline training workflows and real-time online retrieval.
- Established drift detection and automated model retraining pipelines with MLflow and Evidently AI, reducing performance degradation response time by 40%.
- Collaborated with backend teams to integrate candidate generation pipelines with existing search infrastructure via gRPC.
Veridian Health Tech
Sydney, Australia
Machine Learning Engineer
Nov 2019 – Aug 2022
- Developed tabular and text classification models with PyTorch and LightGBM for clinical trial patient recruitment matching.
- Built continuous integration pipelines on GitLab CI running model evaluation benchmarks and data validation tests before registry promotion.
- Optimised training workflows through distributed data-parallel training on multi-GPU AWS instances, reducing training run times from 18 hours to 4 hours.
Aperture Insights
Sydney, Australia
Data Scientist
Aug 2017 – Oct 2019
- Created batch ETL and model scoring jobs with PySpark on AWS EMR to segment enterprise customer behaviour.
- Engineered predictive churn models using scikit-learn and XGBoost, improving identification of at-risk subscriptions across commercial accounts.
- Maintained unit and integration test coverage across shared data science libraries.
Education
The University of Sydney
Master of Information Technology · Computer Science (Specialisation in Artificial Intelligence) · Sydney, Australia · High Distinction · July 2015 – June 2017
Completed postgraduate research coursework focused on probabilistic graphical models and deep neural network architectures for sequential data.
University of New South Wales
Bachelor of Engineering (Honours) · Software Engineering · Sydney, Australia · First Class Honours · Mar 2011 – June 2015
Undertook foundational study in data structures, algorithms, numerical computation, and distributed software systems.
Selected Projects
High-Throughput Vector Retrieval Engine
Oct 2023 – Mar 2024
- Constructed a real-time semantic document search pipeline utilising sentence transformers, Qdrant vector database, and ONNX Runtime.
- Achieved 3x throughput improvements on CPU inference by quantising model weights using dynamic INT8 precision.
Technical Skills
Python
PyTorch
MLflow
Docker
Kubernetes
SQL
FastAPI
Apache Spark
AWS
Git
Languages
English
C2
Mandarin Chinese
B2
Certifications
AWS Certified Machine Learning - Specialty
Amazon Web Services · Aug 2024
Google Cloud Professional Machine Learning Engineer
Google Cloud · Nov 2023
Community & Volunteering
Sydney Tech Mentorship Network
Sydney, Australia · Feb 2023 – Dec 2025
- Volunteer Technical Mentor
- Mentored university students and early-career software developers on transitioning into data science and machine learning roles.
- Assisted in organising local monthly technical presentations and code review workshops.