Flexible AI-Native Wireless DSP Engineer

DeepSig, Inc.; Arlington (VA); Hybrid; USD 120,000 - 180,000; Full time

Job summary

DeepSig Inc. is seeking a Full-time Machine Learning DSP Engineer who will help combine software DSP and ML with state of the art machine learning techniques to create AI-Native wireless systems in simulation and deployed products at scale.

This role develops and validates ML and communications algorithms across modem and wireless sensing solutions, using real RF datasets and collaborating with product teams to enhance RF sensing capabilities based on customer feedback.

Qualifications

• MS or PhD in Electrical/Computer Engineering, Computer Science, or related field.
• Proficient in at least one programming language (Python preferred).
• Familiarity with at least one of the Deep Learning frameworks (e.g. PyTorch, Tensorflow).
• Experience in Deep Learning, RF Sensing, Statistical Signal Processing, Wireless Systems fundamentals, Time/Frequency analysis of signals.
• Ability to work on open ended problems, building candidate solutions and metrics.
• Strong communication and teaming skills to work collaboratively in a small company environment.

Responsibilities

• Build cutting edge Machine Learning models for RF sensing applications.
• Keep up with the latest developments in ML and assess their viability for RF sensing.
• Utilize both Software and DSP techniques to optimize training and inference pipelines.
• Work closely with product managers and business development to improve RF sensing capabilities based on customer feedback.
• Work with real-world RF datasets and ML driven RF systems.

Skills

Python

Deep Learning

PyTorch

TensorFlow

RF Sensing

Statistical Signal Processing

Time/Frequency Analysis

Wireless Systems Fundamentals

C++/CUDA

Education

MS or PhD in Electrical/Computer Engineering, Computer Science, or related field

Tools

TensorRT

CUDA

Job description

DeepSig Inc. is seeking a Full-time Machine Learning DSP Engineer who will help combine software DSP and ML with state of the art machine learning techniques to create AI-Native wireless systems in simulation and deployed products at scale.

This role develops and validates ML and communications algorithms across modem and wireless sensing solutions, using real RF datasets and collaborating with product teams to enhance RF sensing capabilities based on customer feedback.