Saksham Jindal

Machine Learning Engineer

Seattle, Washington, United States6 yrs 9 mos experience
AI EnabledAI ML Practitioner

Key Highlights

  • Expert in deep learning technologies and cloud training.
  • Developed production-ready models for real-world applications.
  • Strong collaboration with cross-functional teams.
Stackforce AI infers this person is a Machine Learning Engineer with a focus on Computer Vision and Robotics.

Contact

Skills

Core Skills

Machine LearningComputer VisionData ScienceRoboticsDeep Learning

Other Skills

pythonpytorchOpenCVData VisualizationArtificial Intelligence (AI)AlgorithmsStatistical ModelingAgileStatisticsForecastingObject-Oriented Programming (OOP)GitPandas (Software)MongoDBDocker

About

🚨 Visit https://saksham.live to view the complete portfolio and access the resume I work at the intersection of Robotics, Computer Vision and Machine learning. Some of the highlights from my work experience and academic tenure • Hands-on working experience with deep learning technologies (Pytorch, Tensorflow, Nvidia CUDA and cuDNN) while training on huge datasets employing distributed training and strategies in resource optimization and training time. I have identically worked on managing an in-house cluster of 5 Nvidia Ti 1080 GPUs and training in the cloud environment (GCP) • Designed and developed several models for real-world use cases involving researching and implementing state of the art deep learning based techniques in image classification, object detection, instance segmentation, 3D reconstruction from stereo images etc. • Developed modular and reproducible pipelines for training and inference generation that could be easily expanded to several use cases and multi-task learning under one unified framework. • Worked in cross collaboration with software engineers (backend developers and devops) to identify the needs, considering coding standards and bring the model into production (Computer Vision API service platform for the end-user and clients). This involved exposing endpoints the model developed through a REST endpoints and subsequent model monitoring • Collaborated with the stakeholders and product managers to understand the requirements of the business and how to translate that into a ML/DL Engineering problem. Actively working and open to consulting opportunities to work as a Data Scientist, Machine Learning Engineer, Deep Learning Engineer, Research Scientist or Applied Scientist in Computer Vision (CV), Natural Language Processing (NLP), Speech Recognition, Recommendation Engine, Search Ranking, Content Personalization, Route Optimisation, Fraud Detection, Time Series and Forecasting etc. Link to Portfolio: https://github.com/sakshamjindal Feel free to drop a note at saksham.jindal[at]outlook.com / jindal.saksham[at]outlook.com

Experience

6 yrs 9 mos
Total Experience
1 yr 6 mos
Average Tenure
7 mos
Current Experience

Pinterest

Machine Learning Engineer II

Nov 2025 – Present · 7 mos · Seattle, Washington, United States · Remote

  • RL for Ads Retrieval
Machine LearningComputer Vision

Metropolis technologies

Machine Learning Enginer II

May 2024 – Nov 2025 · 1 yr 6 mos · Seattle, Washington, United States · Hybrid

Machine LearningComputer Vision

Uc san diego

2 roles

Graduate Research Assistant

Oct 2023 – May 2024 · 7 mos · San Diego, California, United States

  • Currently working with Prof. Lerman on early detection of sepsis in admitted ICU patients using multivariated time series forcecasting with gradient boositing based machine learning and sequence based deep learning
Machine LearningData Science

Graduate Research Assistant

Jan 2023 – Sep 2023 · 8 mos · San Diego, California, United States

  • Currently working with Prof. Michael Yip on robotics manipulation of deformable objects using 3D vision-based forward and invese dynamics for goal conditioned manipulation with appliction in surgical robotics. Also, worked on building SE(3) equivariant and deformation invariant neural representations for tasks such as grasping, manipulating, or interacting with deformable objects
  • Tools and Technologies : python, pytorch, pytorch lightning, OpenCV
pythonpytorchOpenCVRoboticsComputer Vision

Tomtom

Senior Applied AI (Map Making Platform)

Nov 2021 – Aug 2022 · 9 mos · Pune District, Maharashtra, India

  • Worked on Applied AI at TomTom, Inc. on building computer vision and machine learning models for aerial perception, automated integration and generation of road geometry using satellite image data for ingestion into real-time navigation unit of TomTom’s ADAS navigation maps .
  • Aerial perception for extraction and rectification of road network: Developed and productionized vision transformer (for semantic segmentation) and point cloud registration models using road geometry data and GPS probes traces overlaid on satellite imagery to extract directions of transversal and augmentation of road networks in navigational unit of ADAS map
  • GPS probe classification for detection of mode of transportation: Implemented pre-processing, trajectory segmentation and feature engineering on GPS probe traces and developed classification models using LightGBM for 24+ cities from 10 countries
Machine LearningComputer Vision

Github

Open Source Projects

Sep 2019 – Jul 2020 · 10 mos · Greater London, England, United Kingdom

  • Tools Used: python, pytorch, openCV, fastai, numpy, matplotlib, visdom plot
  • Road Lane Marking Detection on BDD100K dataset
  • Implemented and experimented with deep learning architectures for semantic segmentation
  • Architectures implemented included Deeplabv3, Enet, ERFNet, SCNN under a unified pipeline
  • Developed the data injestion (dataloader) module for train-test time image augmentation
  • End-to-end framework for dataloading, model training and inference with difference parameter
  • Key training strategy included using transfer learning on pre-trained weights
  • Code with summary, here: (https://github.com/sakshamjindal/Lane-Detection)
  • Face Recognition using One Shot Learning : Applications include face verification system where a person is validated (accepted or rejected) on single image of face
  • Developed Facenet-based face-verification model trained on face embedding using triplet loss
  • Augmentations included affine transformations, random rotations and brightness adjustments
  • Transfer learning using pretrained Inception-resnet-v1 weights coupled with triplet loss
  • Model outputs a similarity score between 'test face' and 'faces already in system'
  • Code with summary, here: (https://github.com/sakshamjindal/Face-Matching)
Deep LearningComputer Vision

Fractal

Applied ML (Computer Vision and Deep Learning)

Jun 2018 – Oct 2021 · 3 yrs 4 mos · Greater London, England, United Kingdom

  • Worked with the Image and Video Analytics team at Fractal on the product offering of a platform for image and video understanding using deep learning and computer vision. Also, worked with Fortune 500 clients such as Sky UK , Mars and Procter & Gamble on projects involving forecasting and predictive analytics using machine learning.
  • Researching and Prototyping of solutions in Computer Vision using Deep Learning algorithms Responsibilities included development of production ready code and training pipelines
  • Working on active projects included
  • Object detection on aerial images (drones) using YOLOv3 algorithm for surveillance tasks
  • Scene Image Classification using Inceptionv3 and Senet in context of product placement
  • Instance segmentation (using MaskRCNN) for workflow automation for a CPG client
  • Digitisation of document (using OCR) for a federal client and insurance organisation
  • Tools Used: python, pytorch, keras, opencv, docker, flask, nginx, gunicorn
  • Forecasting and Time Series Analysis for a online streaming service on Google Cloud Platform
  • Data harmonisation and feature engineering on TBs on structured data using GCP Bigquery
  • Automated end-to-end ML worflow using orchestration toolbox Airflow (Cloud Composer)
  • Developed forecasting models to predict cancellation rate and key driver for cancellations
  • Visualised monthly and annual forecast with KPIs on visualisation toolbox (DataStudio on GCP)
  • Tools Used: pandas, numpy, statsmodel, prophet, joblib, Airflow, Bigquery, AI platform, DataStudio
  • Multi-touch attribution of advertisement campaigns for a leading online retailer
  • Harmonised data of transaction, campaign details, product price and demographics of 40000 households
  • Developed machine learning models to study the impact and quantify the contribution of touch points
  • Engaged with business stakeholders to build key performance indicators
  • Tools Used: pandas, numpy, statsmodel, joblib, Airflow, Bigquery, AI platform, DataStudio
Machine LearningDeep Learning

Education

UC San Diego

Master of Science - MS — Robotics

Sep 2022 – Mar 2024

Indian Institute of Technology, Kharagpur

Master of Technology - MTech — Ocean Engineering and Naval Architecture

Jan 2017 – Jan 2018

Indian Institute of Technology, Kharagpur

Bachelor of Technology - BTech — Ocean Engineering and Naval Architecture

Jan 2013 – Jan 2017

Cambridge School Srinivaspuri

High school

Jan 1998 – Jan 2013

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