Sr. Staff ML Researcher, LLM Algorithmic Optimization
$175k - $265k • Santa Clara • FullTime
Posted 3mo ago
Job Location
Santa Clara
Tech Stack
Remote Work Policy
On-site
Employment Type
FullTime
Categories
Machine Learning Engineer
About the job
d-Matrix is seeking a Senior Staff ML Researcher to join our Algorithms team and develop new methods for running large language models efficiently on our DNN accelerators. This is a hands-on research role at the intersection of machine learning, applied mathematics, numerical computing, and AI hardware. You will invent, evaluate, and implement algorithmic techniques that improve LLM inference performance while preserving model quality, with a focus on challenges such as numerical precision, model compression, memory efficiency, and efficient execution on accelerator hardware. You will work closely with mathematicians, ML researchers, ML engineers, compiler engineers, and hardware architects to translate research ideas into practical solutions for cutting-edge generative AI workloads.
Responsibilities
- Research and develop algorithmic and numerical techniques to improve LLM inference latency, throughput, memory efficiency, and accuracy on d-Matrix DNN accelerators.
- Design and evaluate methods like low-precision quantization, model compression, sparsity, pruning, distillation, and low-rank approximation for efficient neural-network execution.
- Optimize key transformer inference workloads, including matrix operations, attention mechanisms, and KV-cache utilization, considering model characteristics and hardware constraints.
- Analyze tradeoffs between model quality, numerical precision, memory footprint, compute efficiency, and system performance.
- Build high-quality research prototypes and evaluation frameworks in Python to test new ideas.
- Collaborate with hardware, compiler, and software teams to map algorithms effectively to accelerator capabilities.
- Stay current with advances in efficient LLM inference, neural-network optimization, numerical methods, and hardware-aware machine learning, and apply relevant developments to products.
Requirements
- Master's degree or PhD in Computer Science, Mathematics, Statistics, Physics, Electrical Engineering, or a related quantitative field.
- 5+ years of relevant hands-on experience.
- Strong foundation in machine learning and applied mathematics.
- Ability to apply mathematical or numerical methods to practical ML problems.
- Hands-on experience developing and evaluating machine-learning algorithms, models, or research prototypes.
- Strong Python programming skills and experience with object-oriented code design.
- Experience with modern machine-learning frameworks such as PyTorch, JAX, or TensorFlow.
- Experience improving the performance, efficiency, or quality of machine-learning models through algorithmic or numerical techniques.
- Familiarity with neural-network architectures.
- Ability to design rigorous experiments, analyze results, and communicate technical findings clearly to cross-functional partners.