AI Inference Infrastructure Engineer
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Key skills for this role
About the Role
Dizzaract is seeking an AI Inference Infrastructure Engineer to build the core engine for a distributed inference network. Requires deep systems programming experience (Go, Rust, C/C++), GPU programming (CUDA, ROCm), and expertise in distributed systems and performance optimization.
Key Skills for This Role
Responsibilities
- Design and implement low level runtime systems (Rust/C) to manage model execution, memory allocation, and request batching across distributed compute
- Develop and optimize tensor operations and custom kernels (CUDA, Triton) to maximize hardware utilization
- Implement advanced inference techniques such as continuous batching, speculative decoding, and paged attention
- Design routing and load balancing systems across geographically distributed nodes to ensure availability and efficiency
- Build fully automated deployment and recovery systems ensuring zero manual intervention and self healing network behavior
Requirements
- Strong experience in systems programming (Go, Rust, or C/C++)
- Proven experience working with performance critical systems
- Deep understanding of GPU programming (CUDA, ROCm) and hardware level optimization
- Strong knowledge of deep learning architectures (Transformers, Mamba) and tensor execution
- Experience building distributed systems with high concurrency and low latency
- Ability to work close to the hardware and optimize execution pipelines
- Strong problem solving skills and attention to performance bottlenecks
Full Job Posting
About the Company
- Dizzaract is a product driven company operating at the intersection of gaming, digital platforms, and AI.
- We build and scale multiple products — including Farcana 2.0, Gamed, and FAR Labs — each exploring a different space, but united by a shared approach: moving fast, staying curious, and focusing on things that people actually use.
- We operate as a collaborative, non hierarchical team where ideas are valued based on their impact, not their origin, and where AI is embedded across everything we build — from infrastructure to product decisions.
About the Role
- FAR Labs is building FAR AI, a distributed inference network that turns idle GPU capacity into usable compute for developers, businesses, and AI products.
- We’re looking for an AI Inference Infrastructure Engineer to build the core engine powering this system — from low level runtime to distributed orchestration.
- This is a deeply technical role focused on performance, efficiency, and control over hardware.
Who You Are
- Systems Engineer: You have deep experience in systems programming and care about performance at every layer.
- Performance Optimiser: You understand how to extract maximum efficiency from hardware — not just make things work.
- Infrastructure Builder: You’ve built or contributed to distributed systems where latency and throughput matter.
- Low Level Thinker: You’re comfortable working close to hardware — memory, kernels, execution pipelines.
- Pragmatic Engineer: You avoid unnecessary abstractions and focus on what actually improves performance.
Responsibilities
- Core Engine Development: Design and implement low level runtime systems (Rust/C) to manage model execution, memory allocation, and request batching across distributed compute.
- Hardware Aware Optimisation: Develop and optimise tensor operations and custom kernels (CUDA, Triton) to maximise hardware utilisation.
- Inference Performance: Implement advanced inference techniques such as continuous batching, speculative decoding, and paged attention — tailored to our architecture.
- Decentralised Orchestration: Design routing and load balancing systems across geographically distributed nodes to ensure availability and efficiency.
- Autonomous Infrastructure: Build fully automated deployment and recovery systems — ensuring zero manual intervention and self healing network behaviour.
Requirements
- Strong experience in systems programming (Go, Rust, or C/C++).
- Proven experience working with performance critical systems.
- Deep understanding of GPU programming (CUDA, ROCm) and hardware level optimisation.
- Strong knowledge of deep learning architectures (Transformers, Mamba) and tensor execution.
- Experience building distributed systems with high concurrency and low latency.
- Ability to work close to the hardware and optimise execution pipelines.
- Strong problem solving skills and attention to performance bottlenecks.
What We Offer
- Real ownership and direct impact on a global AI infrastructure product
- Fast execution, low bureaucracy, and a highly collaborative, idea driven team
- Exposure to cutting edge AI, distributed systems, and hardware level optimisation
- Competitive salary with performance based incentives
- 24 days annual leave, plus public holidays
- Health insurance
- Modern office in Yas Creative Hub
- Continuous learning through real world problem solving — not just theory
- The opportunity to shape what you’re working on and influence product direction
- A diverse, open minded team where ideas are genuinely heard
Work Location
- In person
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