Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Senior RF Data Scientist / Research Engineer

Adria Solutions
Cambridge
2 days 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 hardwaresoftware 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 (timefrequency 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 hardwaresoftware product development
Interested? Please Click Apply Now!Senior RF Data Scientist / Research Engineer Near Cambridge
TPBN1_UKTJ

Related Jobs

View all jobs

Senior RF Data Scientist / Research Engineer

Senior RF Data Scientist / Research Engineer

Senior Research Scientist: Data Science and Machine Learning AIP

Senior Research Scientist: Data Science and Machine Learning AIP

Senior Research Scientist: Data Science and Machine Learning AIP

Senior Research Scientist: Data Science and Machine Learning

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.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.