d-Matrix

Principal LLM Inference Engineer

Santa Clara · Principal

Sponsorship not specifiedDetected 21 days ago

PythonFull-Stack DevelopmentMachine LearningLLMsBusiness DevelopmentElectrical EngineeringResearchCommunication

About the role

We value humility and believe in direct communication.
Our team is inclusive, and our differing perspectives allow for better solutions.
Together, we can help shape the endless possibilities of AI.

Develop and tune custom kernels and operator-level optimizations to maximize throughput and minimize latency.
Drive quantization, sparsity, and batching strategies tailored to D-Matrix computational model.
Build and maintain inference runtimes, serving frameworks, and evaluation tooling.
Partner with product and business development to translate POCs into customer-facing demonstrations.

Bachelor's degree in Computer Science, Electrical Engineering, or a related field, and 10+ years of relevant engineering experience
Strong proficiency in Python and C/C++.
Hands-on experience optimizing LLM inference - attention kernels, KV cache, batching strategies, quantization (INT8/FP8/INT4).
Experience with at least one major inference framework (vLLM, SGLang, TensorRT-LLM, ONNX Runtime, or similar) at a contributor level.
Familiarity with GPU kernel programming (CUDA/Triton) and performance profiling tools.

Experience with heterogeneous compute deployments - scheduling inference workloads across dissimilar hardware (accelerators, CPUs, GPUs).
Familiarity with custom silicon or ASIC-based inference (beyond GPU-only environments).
Experience with distributed inference: tensor parallelism, pipeline parallelism, disaggregated serving.
Contributions to open-source inference or ML systems projects.
Experience with production inference serving at scale (latency SLOs, continuous batching, multi-model serving).
Familiarity with speculative decoding, mixture-of-experts routing, or long-context serving techniques.
Working familiarity with the material in the JAX Scaling Book or equivalent systems-level understanding of modern LLM training and inference.
Why D-Matrix Frontier Group

Our work powers everything from benchmarking and evaluation pipelines to production-grade inference serving.

At d-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology.
We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible.
Our culture is one of respect and collaboration.
We are seeking individuals passionate about tackling challenges and are driven by execution.
Ready to come find your playground?
D-Matrix Frontier Group sits at the leading edge of what's possible with LLM inference on heterogeneous hardware.
Build compelling proof-of-concept systems that demonstrate D-Matrix capabilities to customers, partners, and internal stakeholders.

Employment Policy
d-Matrix is proud to be an equal opportunity workplace and affirmative action employer. We're committed to fostering an inclusive environment where everyone feels welcomed and empowered to do their best work. We hire the best talent for our teams, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status. Our focus is on hiring teammates with humble expertise, kindness, dedication and a willingness to embrace challenges and learn together every day.
d-Matrix does not accept resumes or candidate submissions from external agencies. We appreciate the interest and effort of recruitment firms, but we kindly request that individual interested in opportunities with d-Matrix apply directly through our official channels. This approach allows us to streamline our hiring processes and maintain a consistent and fair evaluation of al applicants. Thank you for your understanding and cooperation.
d-Matrix is proud to be an equal opportunity workplace and affirmative action employer.

This listing is sourced directly from d-Matrix's careers page and normalized into a canonical job model.