Senior RF Data Scientist / Research Engineer

Saffron Walden
1 week ago
Create job alert

Senior RF Data Scientist / Research Engineer – Near Cambridge 

My client, a fast-growing AI company based near Cambridge, is seeking a Senior RF Data Scientist / Research Engineer to work at the intersection of RF hardware, digital signal processing, and machine learning. This hands-on R&D role involves analysing complex RF datasets, developing advanced signal-processing pipelines, and contributing to cutting-edge UAV/drone detection technologies.

You will play a key role in prototyping new sensing capabilities, working with SDRs, designing real-world RF experiments, and integrating machine-learning models into early-stage hardware–software systems. This position is ideal for someone who thrives in fast-paced, iterative prototyping environments.

Key Responsibilities

Analysing raw IQ data from SDR platforms (e.g., bladeRF, USRP) to extract, classify, and interpret RF signal features

Building diagnostic RF analysis tools (time–frequency plots, cyclic spectra, EVM, autocorrelation, constellation tracking, etc.)

Designing RF data-processing pipelines built around practical hardware constraints (bandwidth, ADC limits, gain stages, timing jitter)

Modelling RF front-end behaviour (filters, mixers, LOs, AGC, noise figure) to improve signal integrity and inference accuracy

Developing ML and statistical models for RF classification, anomaly detection, and emitter identification

Prototyping real-time or batch-processing systems in Python (NumPy, SciPy, PyTorch) with potential integration via ZMQ, GNU Radio, or C++ backends

Leading RF data collection, field experiments, and over-the-air testing using drones, wireless devices, and custom transmitters

Requirements

Strong Python proficiency for RF data analysis and prototyping (NumPy, SciPy, matplotlib, scikit-learn, PyTorch)

Solid understanding of DSP fundamentals (FFT, filtering, modulation, correlation, noise modelling, resampling)

Familiarity with SDR frameworks such as GNU Radio, SDRangel, osmoSDR, or SoapySDR

Practical understanding of RF hardware chains (antenna → filters → mixers → ADC) and their impact on baseband data

Experience analysing wireless protocols (Wi-Fi, LTE, LoRa, etc.) and physical-layer structures

Comfortable debugging SDR setups and performing field-based RF data collection

Strong communication skills and ability to work effectively within an iterative R&D team

Desirable

Hands-on experience with SDRs (bladeRF, HackRF, USRP, PlutoSDR) and RF lab equipment (spectrum analysers, VNAs, signal generators)

Experience in passive radar, beamforming, TDoA, Doppler, or direction finding

Familiarity with embedded or real-time systems (FPGA pipelines, GPU acceleration, etc.)

Programming experience in MATLAB, C++, Rust, or similar languages

Knowledge of RF circuit principles (impedance matching, filter design, gain budgeting)

Experience designing or testing antenna arrays for sensing/detection

Publications, patents, or open-source RF/ML contributions

Role Details

Location: Cambridge area (onsite or hybrid depending on project needs)

Department: Research & Prototyping Team

Impact: Direct involvement in early-stage hardware–software product development

Interested? Please Click Apply Now!

Senior RF Data Scientist / Research Engineer – Near Cambridge

Related Jobs

View all jobs

Senior RF Data Scientist / Research Engineer

Senior RF Data Scientist & ML Research Engineer

Senior RF Data Scientist - Applied AI & DSP (Onsite)

Senior RF AI/ML Data Scientist — DSP & SDR Onsite

Senior Data Scientist Research Engineer

Senior Data Scientist Research Engineer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

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

Industry Insights

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

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.