The OpportunityQuadric's GPNPU runs both neural networks and C++ code on a single programmable architecture. As a member of our AI Application team, you will make the GPNPU a first-class backend in the frameworks developers already use, such as llama.cpp, PyTorch, and vLLM. That way, customers can move their applications from GPUs to Quadric with minimal changes. You'll own backend integration from memory and dispatch through serving features, working closely with our kernel, runtime and compiler teams.
What You'll DoIntegrate GPNPU backend into AI compute frameworks (llama.cpp, PyTorch, vLLM, Optimum)
- Implement framework backend interface, memory allocation, compute dispatch, registration and operators/kernels
- Implement model compute pipelines including prefill, decode, batching, multi-sequence and multi-modal
- Support important framework serving features including KV-cache management, paged attention, continuous batching, speculative decoding
Optimize framework performance and accuracy for GPNPU backend
- Reduce host-device dispatch overhead, implement graph capture/replay, integrate efficient kernels, minimize latency
- Support quantization formats such as GGUF (Q4/Q8), GPTQ/AWQ, int8/fp16
- Validate accuracy against reference backends (perplexity, task benchmarks) and drive improvements
What Success Looks LikeWithin your first 6-12 months, you'll:
- Bring up a new LLM/VLM family end to end on the GPNPU backend, with accuracy matching the reference
- Deliver measurable tokens/s or latency gains on a frontier model
- Ship one serving feature (e.g. paged KV cache) in a production release
What We're Looking ForRequired- 3+ years building or extending ML frameworks or inference runtimes
- BS/MS in CS/CE or related fields
- Strong C/C++ and Python
- Solid understanding of framework internals: tensor memory management, graph scheduling, operator dispatch
- Solid understanding of AI compute performance analysis and optimization
- Fluent with AI coding agents (e.g. Claude Code, Cursor), and interested in building agent-driven workflows for backend and model bring-up
Preferred - Experience writing an accelerator backend (CUDA, Metal, Vulkan, Hexagon or another NPU) for an open-source framework
- Contribution to open-source frameworks (PyTorch, llama.cpp, vLLM)
- Understanding of low level AI compute pipelines, CPU/NPU, DMA
- Understanding of graph compilers and kernel fusion
What We OfferThe base salary range for this position is $160,000 to $195,000. This range reflects the full span of levels and geographies at which Quadric hires for this role. The actual base salary offered will depend on a number of factors, including the specific level of the role, years and depth of relevant experience, technical skills and competencies, the criticality of the role to the business, internal equity, and work location. In addition to base salary, this role is eligible for equity and a discretionary annual performance bonus as applicable to the role and level.
In addition to a competitive salary, we offer a variety of benefits to support your needs. The benefits below reflect our US-based offerings; for roles in other locations, benefits vary and are shared during the hiring process. These include:
- Medical, dental, and vision insurance from day one - Premiums covered at 99% for Employees
- Company-paid life Insurance
- Voluntary supplemental life insurance
- STD + LTD insurance
- Commuter support including parking or Caltrain reimbursement. Our office is conveniently located within walking distance of the Caltrain station
- FSA + HSA
- Equity with the business
- Paid Parental Leave
- 401(k) Retirement Plan
- Flexible PTO
- Winter holiday shutdown
- Catered lunch each day in our office
- Downtown Burlingame office location, close to shops, cafes, and local amenities
- Collaborative, low-ego culture with significant ownership and impact
- A work culture focused on innovative disruption
If this role resonates with you, we encourage you to apply even if your experience does not perfectly match every qualification. We value potential, curiosity, and a willingness to learn just as much as direct experience.