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 shouldn't 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 you've carried that work over to a second backend.
Hands-on speculative decoding experience: you've 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 llama.cpp/ggml, 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).
What's On Offer At Opera, you'll 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 Opera's on-device AI, working directly with AI leadership.
Research opportunities:
Contribute to open-source projects and publish technical findings where appropriate.
Principal Engineer, On-Device Inference & GPU Kernels in Warsaw – pracodawca: Opera
Opera to globalna marka internetowa, która oferuje wyjątkowe możliwości rozwoju zawodowego w dynamicznym środowisku międzynarodowym. Pracownicy mogą liczyć na elastyczne godziny pracy oraz atrakcyjne wynagrodzenie, a także na wsparcie w zakresie szkoleń i rozwoju kariery. Dołączając do naszego zespołu w Polsce, stajesz się częścią innowacyjnej kultury pracy, która ceni kreatywność i współpracę.
