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, applied AI, 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 Pipeline 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 data processing pipelines in C, C++ or Rust; own correctness, throughput, latency and memory characteristics from initial design through production operation

• Build the cryptographic components of the pipeline layer — signing and verification, key derivation, content hashing and chain-of-custody primitives — that underpin the platform's provenance and trust systems

• Design and implement security enforcement and moderation automation pipelines: content inspection, policy evaluation and classification processing at the data path

• Define and maintain the performance envelope for pipeline components; profile, benchmark and optimise hot paths; produce specifications and standards that other engineers can build to

• Collaborate with security and compliance to ensure pipeline outputs satisfy the platform's COPPA, GDPR and brand integrity requirements at the data processing layer

• Work with infrastructure engineers on the deployment characteristics of pipeline services: containerisation, resource constraints, scaling behaviour and network I/O requirements in cloud environments

• Define the interfaces and data contracts that allow ML inference outputs to be consumed safely and correctly within pipeline stages; contribute to the design of inference integration where applicable

• Produce pipeline component documentation, interface specifications and integration guidance for platform engineers consuming or extending pipeline outputs

Required Qualifications

• A degree in computer science or a related discipline, or demonstrated equivalent capability through professional practice

• Deep hands-on experience writing production-quality C, C++ or Rust in a performance-critical context; demonstrable ability to reason about memory layout, throughput, latency and concurrency — not just correctness

• Experience designing and implementing cryptographic systems or primitives in production: hashing, signing, verification, key derivation or MAC construction in C, C++ or Rust; comfort at the algorithm level, not solely via high-level wrappers

• Proven ability to build and optimise data processing pipelines; streaming, batch or hybrid; profiling, benchmarking and performance debugging are a routine part of your work, not an occasional task

• Working knowledge of concurrency and parallelism patterns relevant to high-throughput pipeline engineering: lock-free data structures, async I/O, thread pool design or SIMD

• Familiarity with content provenance or chain-of-custody systems: digital signatures, content hashing or tamper-evident structures in a production or research context

• Clear understanding of security engineering principles as they apply to systems-level code: input validation, bounds checking, memory safety and side-channel awareness

• Comfortable operating across the stack — from low-level component design to integration with cloud-deployed services

• Strong written communication; able to specify interfaces, document pipeline components and explain performance trade-offs to engineers who are not systems specialists

• Sound engineering judgement about when to optimise, when to simplify and when a design is complete

Preferred Qualifications

• Experience integrating ML model inference into a C, C++ or Rust pipeline; ONNX Runtime, TensorRT, LibTorch or equivalent inference runtime, including latency budget management and model versioning

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

• Familiarity with cloud deployment patterns for systems-level services: containerisation, ECS/Fargate or equivalent, resource limit tuning and network I/O at scale

• Working knowledge of Go for pipeline orchestration, tooling or service integration

• Python for data preparation, pipeline testing, benchmarking harnesses or ML workflow integration

• Background in digital rights management, content authentication or IP protection systems

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