Machine Learning DSP Engineer

DeepSig, Inc

$120K — $145K *
Telecommunications & Hardware
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • MS or PhD in Electrical/Computer Engineering, Computer Science, or related field
  • Proficient in at least one programming language (Python preferred)
  • Familiarity with Deep Learning frameworks (e.g., PyTorch, Tensorflow)
  • Experience in Deep Learning, RF Sensing, Statistical Signal Processing, or Wireless Systems fundamentals
  • Ability to tackle open-ended problems and define metrics for solutions
  • Strong communication and teamwork skills for a small company environment

Responsibilities

  • Build cutting-edge Machine Learning models for RF sensing applications
  • Stay updated on Machine Learning advancements and evaluate their applicability to RF sensing
  • Utilize Software and DSP techniques to enhance training and inference pipelines
  • Collaborate with product managers and business development to enhance RF sensing capabilities
  • Work with unique real-world RF datasets and ML-driven RF systems

Benefits

  • Collaborative and fun small-team culture
  • Opportunities for participation in scientific publications and conferences
  • Flexible schedule and strong work/life balance
  • Employee stock option grant program
  • Welcoming and inclusive work environment
Full Job Description
Job Type

Full-time

Description

Type: Full-Time (W2) On-Site/Hybrid, Arlington, VA

We are seeking Full-time Machine Learning DSP Engineer who will help combine elements of software digital signal processing and software-defined modems with state of the art machine learning techniques to create the next generation of AI-Native wireless communications systems in simulation and in real deployed products at scale. This person will be responsible for developing and validating machine learning and communications system algorithms and software across modem and/or wireless sensing system solutions.

What You'll be Doing
  • Build cutting edge Machine Learning models for RF sensing applications
  • Keep up with the latest developments in the field of Machine Learning and assess their viability for RF sensing applications
  • Utilize both Software and DSP techniques to optimize training and inference pipelines
  • Work closely with product managers and business development teams to improve our RF sensing capabilities based on customer feedback
  • Working with real-world RF datasets and ML driven RF systems which are unique and unrivaled anywhere else in the world

Requirements:
  • 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 etc.)
  • Experience in one or more of the following areas: 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 coming up with appropriate metrics for comparison
  • Strong communication and teaming skills to work collaboratively and productively in a small company environment

Bonus:
  • Experience with advanced signal processing concepts such as multirate signal processing, polyphase filterbanks etc.
  • Prior experience in building Deep Learning models for edge deployment
  • Familiarity with TensorRT
  • Proficiency in C++ and/or CUDA

WORKING AT DEEPSIG

DeepSig is growing its technical team while cultivating a collaborative, agile, and fun small-team culture. We value creativity, knowledge sharing, and employee growth, and we encourage participation in scientific publications, conferences, and open-source software. We offer competitive salaries and benefits, an employee stock option grant program, an environment where we are excited to be transforming and disrupting how signal processing is done with AI/ML, a welcoming and inclusive environment, a flexible schedule, and a great work / life balance.

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