Systems Engineer (Rust)
Dex Partner·Mid-level·Remote · London, England, United Kingdom
Posted Oct 4, 2026·dex.com
The role
Most real-time intelligence platforms demand millions in new hardware before processing a single byte. This company turns existing legacy camera and sensor infrastructure into a real-time operational intelligence layer entirely software-side and privacy-first, building out the architecture to support petabyte-scale stream ingestion and storage.
You will own the core data flow from sensor edge ingestion straight through to the intelligence platform, architecting stream processing, storage systems, and service boundaries in Rust with Python interfaces. This is not glue code or standard CRUD endpoints; it is high-throughput systems engineering where you navigate unfamiliar domains, execute zero-downtime migrations, and make high-stakes tradeoffs without waiting for a complete map.
The work
- Architect and scale distributed stream processing engines, storage layers, and data pipelines built to ingest and index sensor data moving toward petabyte scale.
- Design core service boundaries and APIs across a high-performance Rust backend, integrating Python at the platform API surface.
- Plan and execute zero-downtime migrations and schema shifts on live production systems where data integrity and uptime are non-negotiable.
- Own end-to-end technical decisions across the ingestion-to-intelligence pipeline, balancing low-latency throughput against strict privacy guarantees.
- Set backend architectural patterns, code review standards, and interfaces while leveraging LLM-assisted workflows to accelerate team-wide velocity.
What You Bring
- Strong command of modern statically typed systems, particularly Rust or comparable systems languages, with the ability to ramp up across unfamiliar codebases rapidly.
- Proven track record designing, shipping, and operating high-throughput production systems under real operational constraints, including zero-downtime migrations.
- Demonstrated ability to make sound architectural calls under high ambiguity, balancing structural abstractions against pragmatic delivery when requirements shift.
- Practical fluency with LLM-assisted software engineering workflows, paired with the judgment to know when automated tooling helps and when human precision is required.
