Embedded RF ML Engineer — FPGA, DSP & SDR

Kirintec Limited; Malvern; On-site; GBP 63,000 - 77,000; Full time

Benefits offered by this job

25 days leave

Training opportunities

Flexible working

Medical care package

Company events

Annual bonus

Salary reviews

Casual dress

Free parking

On-site parking

Private medical insurance

Job summary

Kirintec Limited in Great Malvern is seeking a skilled ML specialist to work on embedded RF platforms and EW applications. You will train, optimise, and deploy ML models for hardware targets such as FPGA and embedded processors, and collaborate across teams to deliver robust solutions.

You will apply offline training, model quantisation, and standard libraries like TensorFlow, PyTorch, and NumPy, ensuring practical, field-ready outcomes while aligning with ISO standards.

Qualifications

• Specialist in ML with DSP and algorithms
• Demonstrate best practices for training and deploying ML models
• History of using ML/algorithms/statistical techniques to solve real-world problems
• Experience with FPGA or embedded processors preferred
• Practical experience with DSP in RF comms or similar domain
• Expert knowledge of Python and libraries such as NumPy, PyTorch, TensorFlow
• Knowledge of Git, Docker, Jenkins
• Experience with SDR systems

Responsibilities

• Develop ML techniques for embedded RF platforms and desktop systems used in Electronic Warfare
• Develop offline using standard libraries such as TensorFlow and use quantisation for target hardware compatibility
• Deliver concept demonstrators up to field trial level
• Identify new techniques to advance the product
• Provide ML and algorithms expertise to the company
• Carry out other duties to support company objectives
• Work to ISO standards

Skills

ML

DSP

Algorithms

Python

TensorFlow

PyTorch

Git

Docker

Jenkins

Embedded

FPGA

VHDL

SDR

Tools

Docker

Git

Jenkins

TensorFlow

PyTorch

NumPy

Python

VHDL

Job description

Kirintec Limited in Great Malvern is seeking a skilled ML specialist to work on embedded RF platforms and EW applications. You will train, optimise, and deploy ML models for hardware targets such as FPGA and embedded processors, and collaborate across teams to deliver robust solutions.

You will apply offline training, model quantisation, and standard libraries like TensorFlow, PyTorch, and NumPy, ensuring practical, field-ready outcomes while aligning with ISO standards.