Senior Systems Software Engineer

LEGO Digital Play will bring the LEGO brand into digital entertainment in new, innovative, and wholly-owned ways. Our mission is to ensure the LEGO Brand remains as powerfully a part of children’s lives in the coming decades as it has ever been. We aim to reach every kid on the planet, their parents, and adult fans of LEGO—and provide them with meaningful, magical, and playful new experiences.

Central Technology is the engineering heart of LEGO Digital Play, a venture within the LEGO ecosystem dedicated to pioneering creative and joyful digital experiences. Our teams build the foundational platforms, tools, and capabilities that power LEGO's digital future. We work at the intersection of play and developer tooling to bring the LEGO brand to a new generation of digital builders. It's a rare opportunity to shape something from the ground up within one of the world's most loved brands.

We are at the earliest phases of this new company, offering a unique opportunity to build a new entity for the world's most beloved and trusted brand. Our culture is open, collaborative, intellectually rigorous, and creatively vibrant.

Role Summary

We are looking for a Senior Systems Software Engineer to join our Central Technology team in our London HQ, to start designing and building out our high-performance processing pipelines that sit at the trust layer of our technology.

Key Responsibilities

• Design and implement high-performance content-processing services in C++ or Rust — owning correctness, throughput, latency and memory behaviour from design through production operation.

• Build the provenance layer: content hashing, digital signing and verification, and tamper-evident chain-of-custody structures that let the platform prove where content came from and that it's unaltered. (Integrating and correctly applying established cryptographic libraries — not inventing primitives.)

• Build automated content-safety processing at the data path: content inspection, policy evaluation and classification, including consuming ML model outputs safely within the processing stages.

• Define and hold the performance envelope for these components — profile, benchmark and optimise hot paths — and write the specifications other engineers build to.

• Partner with security and compliance so that what the platform emits meets its COPPA, GDPR and brand-integrity obligations at the data-processing layer.

• Work with infrastructure engineers on how these services deploy and scale — containerisation, resource limits, scaling behaviour, network I/O in the cloud.

• Define the interfaces and data contracts through which ML inference outputs are consumed correctly and safely within the processing stages.

• Produce the component documentation and integration guidance that platform engineers rely on to consume or extend your work.

Required Qualifications

• Strong, hands-on experience writing production C++ or Rust in a performance-sensitive context — you reason fluently about memory, throughput, latency and concurrency, not just correctness. (A degree in computer science or a related field is welcome, but demonstrated equivalent experience counts fully.)

• Experience building and optimising data-processing services — streaming, batch or hybrid — where profiling, benchmarking and performance debugging are routine, not occasional.

• Working knowledge of concurrency and parallelism for high-throughput work — e.g. async I/O, thread-pool design, lock-free structures or SIMD (breadth here is fine; we don't expect all of them).

• A solid grasp of security engineering for systems-level code: input validation, bounds checking, memory safety, and awareness of side channels.

• Comfort operating across the stack, from low-level component design to integration with cloud-deployed services.

• Clear technical writing — you can specify an interface, document a component and explain a performance trade-off to engineers who aren't systems specialists.

• Sound judgement about when to optimise, when to simplify, and when a design is done.

Preferred Qualifications

• Familiarity with content provenance / authenticity systems — digital signatures, content hashing, tamper-evident structures, or the C2PA / Content Credentials ecosystem (whose reference tooling is in Rust).

• Practical command of applied cryptography as a user of it: you can correctly select and integrate vetted crypto libraries for hashing, signing, verification and key handling, reason about their security properties, and avoid common misuse. (You do not need to have implemented cryptographic primitives from scratch.)

• Experience with content-moderation pipelines — automated classification, confidence scoring, policy enforcement, or human-review integration at scale.

• Experience integrating ML inference into a C++/Rust service — ONNX Runtime, TensorRT, LibTorch or similar — including latency budgets and model versioning.

• Cloud deployment patterns for systems-level services — containerisation, ECS/Fargate or equivalent, resource-limit tuning, network I/O at scale.

• Go for orchestration/tooling, or Python for data prep, test and benchmark harnesses, or ML workflow glue.

• Background in DRM, content authentication or IP-protection systems.

• Experience in games, interactive entertainment or media platforms where provenance and content safety at scale are operational realities.