Senior Systems Engineer supporting DIAs National Digital Exploitation and OSINT Center for a tech company serving public sector clients. Manage systems engineering tasks and lead a scrum software development team.
IC
Onsite Principal Engineer – Distributed AI Systems Architecture, Heterogeneous Compute
Posted 3 hours ago
About the role
- Principal Engineer defining and architecting distributed AI systems across heterogeneous compute platforms at Intel. Focusing on dynamic execution and optimization of large-scale AI computation graphs.
Responsibilities
- Define a runtime model for executing AI workloads as distributed computation graphs across heterogeneous resources
- Design abstractions for graph representation, dependencies, and execution semantics
- Enable dynamic scheduling and execution across CPUs, GPUs/specialized accelerators, and IPUs/FNICs.
- Architect systems where state (e.g., KV cache) is a first-class concern in scheduling and execution
- Develop mechanisms to analyze AI computation graphs and classify stages by: compute intensity, memory bandwidth requirements, communication cost, latency sensitivity
- Architect frameworks that treat specialized accelerators (e.g., dataflow engines) as first-class execution targets
- Design runtime strategies for Mixture-of-Experts (MoE) models, including: expert placement, routing locality, load balancing vs data movement trade-offs
- Define observability and telemetry models for distributed AI execution
- Build feedback loops that continuously optimize placement, scheduling, and resource utilization
Requirements
- Bachelor's or BS degree in Computer Science, Software Engineering, or a related specialized field, or equivalent experience per business needs.
- 12-plus years of experience with a Bachelor's degree
- Proven expertise in defining and implementing software architectures for AI frameworks, protocols, and algorithms.
- Deep experience in systems architecture, high-performance computing, or distributed systems
- Strong background in parallel or data-parallel computation models
- Experience with heterogeneous compute environments (CPU, GPU, DSP, or accelerators)
- Proven ability to design end-to-end systems from abstraction through implementation
- Strong understanding of performance trade-offs across compute, memory, and interconnect.
Benefits
- competitive pay
- stock bonuses
- benefit programs which include health
- retirement
- vacation
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