Siddharth Agrawal
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Machine Learning Engineer at Meta
I have 6+ years of industry experience, with 5 of those years working on applied machine learning/computer vision problems. Most of my applied machine learning experience was acquired while working on complex computer vision problems in the security industry for Calipsa. In early 2022, we were acquired by Motorola Solutions as a result of our technology lead in the space. I was a critical part of the machine learning team that developed those core components. Working in a start-up has given me end-to-end experience in solving machine-learning problems, including data collection, cleaning/sorting of the data, formulating solutions, defining evaluation metrics, prototyping using state-of-the-art algorithms, running experiments to tune model architectures, productionising and maintaining the resultant models, etc. I have worked in a number of areas including object detection, semantic segmentation, image classification, active labelling, etc. I’m also familiar with good software engineering practices having worked as a software engineer at Amazon and having made open-source contributions to machine learning libraries like TensorFlow and mlpack. My work at Calipsa (and now at Motorola) was conducted in the Python ecosystem using libraries such as Tensorflow, NumPy, SKLearn, OpenCV, etc. I have a Bachelor's and a Master's degree in computer science. My Master’s degree was with a specialisation in machine learning, which is how I got into the field.
Indian Institute of Science (IISc)
M.E., Computer Science and Engineering
January 1, 2015 – January 1, 2017
Birla Institute of Technology and Science, Pilani
B.E.(Hons.), Computer Science and Engineering
January 1, 2010 – January 1, 2014
Meta
Machine Learning Engineer
May 1, 2025 – Present
London Area, United Kingdom · Hybrid
Motorola Solutions
Principal Research Scientist
April 1, 2023 – May 1, 2025
London, England, United Kingdom
Motorola Solutions
Machine Learning Tech Lead
April 1, 2022 – April 1, 2023
London, England, United Kingdom
Calipsa
Machine Learning Tech Lead
January 1, 2021 – April 1, 2022
Calipsa
Machine Learning Engineer
December 1, 2017 – January 1, 2021
IBM Research
Research Engineer
July 1, 2017 – November 1, 2017
Bengaluru Area, India
Amazon
Software Development Engineer
July 1, 2014 – June 1, 2015
Bengaluru Area, India
MLPACK
Contributor (Google Summer of Code 2014)
May 1, 2014 – August 1, 2014
Implementation of Generative Moment Matching Networks
March 1, 2016 – April 1, 2016
This project involved the study and implementation of the following research paper: https://arxiv.org/abs/1502.02761. Implemented both code space and data space networks as mentioned in the paper. The models were trained on the MNIST and LFW datasets. Snapshots of samples drawn from the trained networks can be found by following the project URL. The implementation was done in Python, primarily using TensorFlow.
Knowledge Base Completion using Neural Tensor Network
September 1, 2014 – Present
This project involved the study and implementation of the following research paper: http://nlp.stanford.edu/~socherr/SocherChenManningNg_NIPS2013.pdf. The neural tensor network was trained and tested using the WordNet dataset. The model gives an accuracy of 81% after training with three tensor slices. The code was written in Python using the NumPy and SciPy libraries.
Image Recognition using Convolutional Neural Network
May 1, 2014 – Present
Convolutional Neural Network is a popular technique for general image recognition. This project involved developing an implementation of the technique. The network was trained an tested using a subset of the STL dataset, which contained only four objects. The model gives an accuracy of 80%. The code was written in Python using the NumPy and SciPy libraries.
Collaborative Filtering Package Improvements
May 1, 2014 – August 1, 2014
Collaborative Filtering is a popular technique used in Recommender Systems. This project included implementing matrix factorization techniques to improve upon the Collaborative Filtering package in MLPACK, a Machine Learning library written in C++. QUIC-SVD and Regularized SVD algorithms were implemented as a part of this project.
Triplebyte Certified Machine Learning Engineer
Triplebyte
June 24, 2026 – Present
Interactive 3D Graphics
Udacity
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Top Mentor (5.0 / 5.0)
MentorCruise
June 24, 2026 – Present
edX Verified Certificate for Foundations of Modern Finance II
edX
June 24, 2026 – Present
CS188.1x: Artificial Intelligence
edX
June 24, 2026 – Present
Quantum Mechanics and Quantum Computation
Coursera
June 24, 2026 – Present
edX Verified Certificate for Derivatives Markets: Advanced Modeling and Strategies
edX
June 24, 2026 – Present
edX Verified Certificate for Mathematical Methods for Quantitative Finance
edX
June 24, 2026 – Present
edX Verified Certificate for Financial Accounting
edX
June 24, 2026 – Present
edX Verified Certificate for Foundations of Modern Finance I
edX
June 24, 2026 – Present
Linear and Integer Programming
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from neural networks to collaborative filtering, and their experience across different companies (Meta, Motorola, Calipsa, IBM, Amazon) suggest adaptability and a broad technical interest. While the target role is 'Data Analyst', the candidate's background is heavily skewed towards Machine Learning Engineering and Research. This indicates a potential mismatch in the specific day-to-day responsibilities, as a Data Analyst role typically focuses more on data extraction, transformation, visualization, and statistical analysis rather than ML model development and deployment. However, the underlying analytical and data manipulation skills are transferable.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight leadership in technical roles, problem-solving (e.g., reducing false alarms, improving efficiency), and a focus on delivering revenue-generating products. These indicate strong operational fit and practical application of technical skills. The certifications in finance also suggest a breadth of interest and self-driven learning.