Last updated: 7th June 2024

Dheeraj Rajaram Reddy  London

Work Experience

Member of Technical Staff June 2024 - Present
  • Pretraining
Member of Technical Staff July 2020 - May 2024
  • SpotIQ: ML engine
  • Cortex: Time-series forecasting
  • Falcon: OLAP in-memory JIT database
  • Build systems: Bazel, Gradle
  • LLMs: Fine-tuning, scaling, deployment
  • Production Engineering: Search + Data infra
  • C++ infrastructure/core libs
  • CI/CD: Jenkins, Tooling
Open Source Maintainer and Core Contributor June 2019 - May 2020
  • Implemented several core layers, optimizers, models, and the first TF2.0 native implementation of CRFs.
  • Maintained the CI and package release pipelines.
Intern January 2020 - June 2020
  • Containerized execution engine.
  • Query-aware data-generation.
  • CI and build systems.
Intern May 2019 - June 2019
  • Rewrote a Spark data-processing pipeline to make it 3x faster and less prone to Java OOM errors.
  • Added deep learning support for their automated ML platform using TensorFlow.
  • Built a distributed KNN algorithm using Hybrid Spill-trees.
Team Leader and Motion-Planning Head February 2017 - February 2019
  • Lead a team of 60 engineering students, handling both the technical and managerial aspects of a robotics team.
  • The team placed 1st worldwide at Intelligent Ground Vehicle Competition (IGVC) 2019.
  • Won the Mahindra Rise Prize Challenge for building the best driveless car for Indian roads.
  • Designed, implemented and tested the software stacks of autonomous bots and cars.


Manipal Institute of Technology 2016 - 2020

B. Tech Computer Science and Engineering

  • Minor in computational mathematics

Selected Projects & Contributions

  • Open source: Contributions to various OSS projects, including TensorFlow, PyTorch Glow compiler, etc.
  • Shadesmar: A high-performance C++ IPC library that uses shared memory for communication. Supports RPC and pub-sub.
  • SummaryDB: A database written in Go for storing colossal amounts of time-series data. Uses window-based aggregations for compressing data.
  • Technical blog: Authored several technical articles on programming, designs, concepts, challenging problems I’ve faced, and computers in general.
  • ReiLs: A reinforcement learning framework for faster modular prototyping, deployment and benchmarking of Deep-RL algorithms. Built using TensorFlow, MPI and NCCL.


  • Languages: C, C++, Java, Go, Python, Rust, JS
  • Build Systems: Bazel, CMake, Gradle
  • CI/CD: GitHub Actions, Jenkins
  • Deep Learning: Jax, PyTorch, TensorFlow
  • Parallel Programming: Arm Neon, Intel SIMD, Halide
  • Distributed Computing: Docker + Kubernetes, MPI
  • Miscellaneous: OpenCV, ROS, Thrift, gRPC, Protobuf

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