Mayank Kumar Pal

Software Engineer

Bengaluru, Karnataka, India7 yrs 6 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in machine learning model optimization.
Led development of advanced satellite communication features.
Strong background in AI software for embedded systems.

Stackforce AI infers this person is a Machine Learning Engineer specializing in AI for embedded systems and telecommunications.

Contact

Skills

Core Skills

Machine LearningEmbedded SoftwareAutomotiveTelecommunications

Other Skills

TensorFlowONNXMLpefQNN SDKModel OptimizationReal-Time Operating Systems (RTOS)Linux AndroidHypervisorHigh-Level DesignDebuggingPower ControlSatellite ModemsLTENR5C (Programming Language)

Experience

7 yrs 6 mos

Total Experience

3 yrs 9 mos

Average Tenure

5 yrs 10 mos

Current Experience

Qualcomm

3 roles

Senior Lead Engineer

Promoted

Nov 2025 – Present · 7 mos

TensorFlowONNXMLpefQNN SDKModel OptimizationEmbedded Software+26

Senior Engineer

Promoted

Oct 2022 – Nov 2025 · 3 yrs 1 mo

I work in AI Software team at Qualcomm, where I am involved with Neural Processing (QNN) SDK development to fine-tune machine learning models for on-device performance on Qualcomm's NSP. This involves everything from onboarding new models to optimizing and debugging them for best performance and accuracy.

C++AutomotiveMachine Learning

Engineer

Aug 2020 – Oct 2022 · 2 yrs 2 mos

RF Software Engineer
Worked in FBRx team which is responsible for correcting the transmit power of the modems.
Worked on bringing up the satellite-on-snapdragon feature which helps to send emergency short text messages in absence of cellular connectivity.
Worked on NR+NR DSDA feature which allows two simultaneous 5G connections to be active at
same time.

Satellite ModemsPower ControlEmbedded SoftwareTelecommunications

Tata consultancy services

Research Scholar

Dec 2019 – May 2020 · 5 mos · Noida, Uttar Pradesh, India

Worked on Open CL based framework to deploy neural network on FPGAs and GPUs for low-power devices.

Machine Learning

Elucidata

Software Engineering Intern

May 2019 – Jul 2019 · 2 mos · New Delhi Area, India

1. Reduced the time of scaling up of the cloud server which earlier used to take very long before user request gets served. The server was deployed using Kubernetes on Google Cloud Platform.
2. Automated the build, test and deployment process by building pipelines for Continuous Integration and Continuous Deployment. Further, docker images were created to remove cross-platform dependence.

Philips

Machine Learning - Research Intern

May 2018 – Jul 2018 · 2 mos · Bengaluru Area, India

1. Intent Classification - Build a pipeline to systematically interact with authoring kit APIs to match/identify the intent of the user query using Intent Classification models trained in Keras. The same trained models were used to suggest alternative queries too.
2. Named Entity Recognition - Identified/Extracted named entities like name, locations, organization, quantities etc. from the unstructured text to anonymize personal data using models trained in Keras.

Machine Learning