Maya Lin
Machine Learning Engineer specializing in production inference systems, MLOps, and scalable pipelines
Halcyon Cloud Platforms
Dear Hiring Team,
I am writing to express my interest in the Machine Learning Engineer role at Halcyon Cloud Platforms. With a background spanning distributed inference systems, model lifecycle automation, and cloud deployments, I am keen to contribute to Halcyon's engineering initiatives focused on scalable and resilient machine learning infrastructure.
In my current position at Kestrel Analytics, I architected and deployed containerised inference microservices using FastAPI, Triton Inference Server, and Kubernetes. This system reliably serves 1.2M daily recommendations with sub-45ms p95 latency. Additionally, I established automated drift detection and model retraining workflows using MLflow and Evidently AI, which reduced our response time to performance degradation by 40%. These responsibilities have reinforced my focus on designing low-latency architectures that maintain strict operational reliability under production workloads.
Prior to this, at Veridian Health Tech, I developed tabular and text classification models with PyTorch and LightGBM for clinical trial recruitment matching. To accelerate model iteration, I optimised training workflows using distributed data-parallel execution across multi-GPU AWS instances, lowering overall run times from 18 hours to 4 hours. Combined with my experience building automated data validation and CI testing benchmarks on GitLab CI, this work deepened my approach to reliable model delivery and pipeline optimisation.
Halcyon's focus on building high-performance inference platforms and reliable MLOps tooling aligns closely with my technical background. I welcome the opportunity to discuss how my experience in serving infrastructure and automated deployment pipelines can support your engineering team.
Yours sincerely,
Maya Lin