Principal Engineer, On-Device Inference & GPU Kernels

Location: Wrocław Warsaw (Hybrid)

Were looking for a hands-on Principal Engineer to join our AI & Browser Innovation Hub in Wrocław focusing on high-performance on-device AI inference and GPU this role youll work at the intersection of advanced AI research and low-level systems engineering developing solutions that make language models faster and more efficient on consumer hardware. Working closely with Operas AI leadership youll take ownership of performance-critical components from speculative decoding and GPU kernels to quantisation and memory management. Youll have the opportunity to experiment with emerging techniques solve complex engineering challenges and contribute to AI capabilities used by millions of Opera users.

Role & Responsibilities

• Speculative decoding. Train tune and ship draft models for the models we run block-diffusion drafters (DFlash2-like) semi-autoregressive drafters with confidence-scheduled verification (DSpark) EAGLE-style heads and multi-token prediction and keep pushing acceptance length up and drafting cost down on consumer hardware.

• Kernels. Write fuse and tune kernels for attention quantised matmul MoE routing sampling and draft verification across Metal/MLX CUDA Vulkan and OpenVINO.

• Quantisation. Design mixed-precision schemes for weights activations and KV cache. Match formats to the hardware from 4-bit integer on integrated GPUs and NPUs to FP8 FP4 and microscaling formats on newer GPUs and prove what each one costs in quality before it ships.

• Prefill and decode. Push time-to-first-token and tokens per second with flash and paged attention efficient handling of sliding-window and hybrid attention chunked prefill and kernel-level scheduling.

• Tiered memory. Build hierarchical KV and context caching that spans GPU memory system RAM and SSD with prefix reuse smart eviction and prefetching that hides I/O so long contexts and multiple models fit on machines that shouldnt be able to hold them.

• Making it work everywhere. Build the test matrix performance telemetry and regression gates that catch a slow driver a wrong numerical result or a stalled UI before users do.

• Instrumentation. Expose hooks into the runtime such as activations and attention so research and security work can see inside models without giving up the speed of fused kernels.

Job Requirements

• Deep hands-on GPU programming in at least one of CUDA Metal/MLX or Vulkan compute plus evidence that youve carried that work over to a second backend.

• Hands-on speculative decoding experience: youve implemented or trained drafters and understand what actually drives acceptance rate and end-to-end speed-up.

• A track record of making real inference workloads measurably faster in tokens per second latency or memory with numbers you can explain.

• Strong command of quantisation and its effect on model quality including how you measure it.

• Discipline about correctness. You validate numerics against a reference and measure quality not just speed.

Nice to Have

• Contributions to MLX vLLM SGLang ONNX Runtime OpenVINO or similar.

• Experience with drafter-training frameworks or published work on speculative or parallel decoding.

• Quantisation research or tooling: GPTQ AWQ k-quants MX formats or quantisation-aware fine-tuning.

• KV cache compression offloading or paging at the systems level.

• Experience with NPUs (Intel Qualcomm AMD Apple Neural Engine).

Whats On Offer

At Opera youll join a collaborative international team with a flat structure direct communication and opportunities for professional growth.

• Advanced computing resources: Access to Apple Silicon NVIDIA (including Blackwell-class) AMD and Intel hardware plus cloud GPUs for model training.

• Technical ownership: Lead performance-critical development of Operas on-device AI working directly with AI leadership.

• Global impact: Build AI solutions used by hundreds of millions of browser users across diverse hardware platforms.

• Research opportunities: Contribute to open-source projects and publish technical findings where appropriate.

Interested

Wed love to hear from you! Applications are reviewed on a rolling basis so we encourage you to apply soon. Please submit your CV in English.