Specialised AI Engineer

Skills

AcceleratorAdapterCUDACuratingModelingOptimizationPersonal Boundaries

Role

What the job involves

The main requirements, responsibilities and hiring steps.

5+ years building production systems in machine learning distributed systems or high-performance infrastructure
4+ years hands-on experience in at least one core AI systems area in large-scale production environments
Strong hands-on expertise in one core area with working knowledge across others
Proven ability to design optimise and operate systems at scale balancing latency throughput cost and model quality
Deep understanding of transformer architectures LLMs and/or multimodal models in production
Strong proficiency in Python and PyTorch for production-grade ML systems
Experience with distributed compute and training paradigms including data model parallelism sharding and scheduling
Experience with GPU or accelerator optimisation and system-level performance tuning
Experience building or operating production inference or training systems at scale
Ability to design clean abstractions APIs and reusable systems for other engineers
Strong engineering fundamentals and maintainable well-tested production-quality code

Design and optimise scalable AI platform systems for training post-training evaluation and low-latency inference under strict performance and efficiency constraints
Build and improve distributed services that support large-scale model workflows including fine-tuning alignment dataset processing and benchmarking
Investigate performance bottlenecks and develop reusable APIs SDKs and tooling that help other engineers use AI services effectively