Senior Backend / Platform Engineer

Our client is a European deep-tech startup developing next-generation radar systems for the detection and tracking of small, low-flying drones. The team works across radar architecture, RF hardware, signal processing, embedded systems and data-processing software. Engineers have real ownership of technical decisions and a direct impact on system performance and product capabilities. This is a hands-on R&D environment for specialists who want to solve complex engineering problems and contribute to defence technology designed for real-world deployment. What you'll do Pipeline — Build a Python backend that ingests a live I/Q stream and manages queues, buffers, processes and data flow. Real-time path — Move large I/Q matrices into the AI core with minimal latency using shared memory, zero-copy and ring / ping-pong buffers — not REST. Integration — Wrap and profile the team's math/AI modules (NumPy, ONNX Runtime / PyTorch) and remove bottlenecks together with the signal engineers. Data layer — Design the schema for tracks, events and metadata; async writes via asyncpg / SQLAlchemy async. Raw I/Q stays in RAM; raw logs go to cold storage. API & UI — FastAPI for commands, auth and status; secure WebSockets streaming tracks to a simple operator dashboard (Plotly Dash / Streamlit). Infra — Docker / Docker Compose, GitHub Actions CI/CD, shared-memory config in containers, Terraform-ready deployment. Security — TLS to Postgres, HTTPS/WSS, JWT auth, network isolation, secrets handling, least-privilege access. What we're looking for Senior (or strong mid) Python engineer who has built real engineering systems, not only web APIs. Solid with asyncio, multiprocessing and multiprocessing.shared_memory. Comfortable with large data matrices (NumPy) and profiling / optimising Python for latency and throughput. PostgreSQL (asyncpg or SQLAlchemy async), FastAPI, REST, WebSockets. Docker, Docker Compose, Linux, Git / GitHub Actions. Security basics: TLS, HTTPS, WSS, JWT. Nice to have ONNX Runtime / PyTorch inference, CuPy, Numba, SciPy, GPU inference. ZeroMQ / PyZMQ, Zarr / HDF5, structured logging, Prometheus / Grafana. Linux performance tuning (CPU affinity, taskset, psutil), Terraform, Ansible. Any exposure to high-throughput / low-latency systems, SDR, radar, signal processing, streaming or telemetry pipelines. What We Offer Impact Own the core signal-detection system. Your work directly shapes what the product can detect, understand, and act on. Engineering Challenges Worth Solving Work on complex radar, DSP, and signal-processing problems that are typically found only in large R&D organizations or deep-tech companies. You'll tackle hard technical challenges with direct influence on outcomes. A Team You'll Learn From Collaborate closely with scientists, engineers, and operators who combine deep technical expertise with a strong product mindset. No layers of bureaucracy—just smart people solving meaningful problems together. Ownership & Autonomy We're looking for builders, not executors. You'll have the freedom to make decisions, take responsibility, and drive critical parts of the technology forward.