Applied Machine Learning Engineer#

Hi, I’m Arjun, an Applied Machine Learning Engineer with 14+ years of building and shipping ML systems. I work across the whole stack: data and sampling, modelling, distributed training, and low-latency C++ / Python inference at scale.

I’m currently a Senior Applied ML Engineer at Shopify, working on merchant foundation models.

Machine Learning Proficiency#

Areas I’ve worked in across the full project lifecycle:

  • Data & sampling: unbiased datasets via feature-based sampling, hard-negative mining, and synthetic data that matches the real distribution.
  • Modelling: Convolutional, Recurrent, and Transformer architectures; contrastive learning (SimCLR, BYOL, SimSiam); and sequential models.
  • Distributed training: GPU clusters over OpenMPI + RoCE, Torch RPC, and PyTorch Lightning.
  • Calibration: Focal Loss, label smoothing, isotonic regression, and Platt scaling.
  • Optimization: quantization, pruning, distillation, and graph fusing with ONNX, TorchDynamo, and TVM.
  • Inference: scalable, low-latency C++ serving (ONNX, Drogon, Triton) on k8s.

Recent Explorations#

Some things I’ve built recently, and what each one looks at:

  • imba-chess: a high-Elo chess bot that ports HSTU sequential transformers from recommender systems to chess, with policy imitation from Lichess games, a WDL value head, and depth-2 policy-pruned minimax search. It looks at how far sequence models transfer from recsys to strategic games. Build log: Building a Chess Bot with HSTU.
  • muvfde: fixed-dimensional embeddings for multi-vector representations, based on Google Research’s MUVERA. The C++ core is exposed to Python via nanobind and published on PyPI. It trades model size for retrieval latency using a fixed-dimensional encoding.
  • Comparing Online Hyperopts: a benchmark of online hyper-parameter optimization methods on sampling efficiency, latency, and ease of implementation.

Domain Expertise#

Problems I’ve shipped machine learning for:

  • Search & Ranking, Cold-start Recommendations
  • Constraint-based Optimization
  • Speech Processing (Speech-to-Text, Text-to-Speech)
  • Computer Vision (Segmentation, Classification, OCR)
  • Natural Language Processing (Document QA, Classification, Entity Recognition)
  • Contrastive Learning methods

Get in Touch#