FPGA Firmware Engineer (Developing to Senior level depending on experience)

ALL.SPACE
Reading
2 months ago
Applications closed

Related Jobs

View all jobs

Head of Hardware

Senior Electronics Engineer

RF Design Engineer

Electronics Engineer

Senior Electronics Engineer

Embedded Software Engineer

This job is brought to you by Jobs/Redefined, the UK's leading over-50s age inclusive jobs board.

A high number of candidates may make applications for this position, so make sure to send your CV and application through as soon as possible.At ALL.SPACE we are fortunate to produce stand-alone phased-array antennas. This means that we cover the entire electronics spectrum. We design and produce the electronics hardware (power, analogue, digital, and RF), as well as the firmware and software that runs on our hardware. FPGAs are crucial elements of our antennas, and our FPGA firmware designers undertake the whole of the development process: from negotiation of requirements and initial design architecture, through to formal validation and verification of the design. The work is extremely challenging but incredibly rewarding. You will work within a small team of firmware engineers who are experts in their field. Although the primary role is to develop firmware for deployment into an FPGA, you may also get the opportunity to write software for our products and test systems or use Matlab to generate and analyse test data or simulate DSP algorithms. One thing is for sure: you will be contributing to our unique and novel antennas within an enthusiastic and supportive team.Responsibilities:

Develop FPGA firmware to industry standards, that implements signal processing functions, glue logic, and high-speed interfaces.Develop testbenches for FPGA firmware and perform the verification.Develop firmware for complex FPGAs, SoCs, and PLDs.Test firmware on system hardware.Support integration efforts as required.Develop and agree development plans with the team leader and work towards the plan to deliver firmware on time.Review firmware code developed by peers.Contribute towards miscellaneous firmware team activities.Requirements

Education

University degree in Electrical, Communications, or related engineering discipline (first or upper-second class)PhD would be a significant advantage.

Soft skills

Proactive "can-do" mentalityAbility to present information and communicate using well-thought-out responses; both verbal and writtenGood interpersonal skills

Required Technical skills

Robust electronic engineering skillsBinary number arithmeticDigital logic design at Register Transfer Level (RTL)HDL programming (VHDL or Verilog)

Following skills and experience are advantageous

At least two years' experience as a firmware developerFPGA firmware architecture designSignal processing theory of RF signals, ADC and DACDigital electronic designDigital signal processing (DSP) designExperience with Xilinx FPGAs (RFSoC, MPSoC, Ultrascale+, etc.)Experience with Xilinx design suite (Vivado, Vitis, Petalinux, etc.)Use of bench equipment (e.g. spectrum analyser, oscilloscope, RF signal generator, network analyser)LinuxGitTCLC programmingMicrosoft Office Suite

Benefits

Excellent compensation package inclusive of competitive salary, pension, and 25 days holiday in addition to the UK bank holidays.Bonus and stock options package.Career development training opportunities (in-house and external).Hybrid working.Cycle to Work and Employee Referral schemes.24/7 access to discount platform across 900 retailers.24/7 access to the Well-being Centre and Employee Assistance Hotline.Free access to a gym.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.