Research Engineer, Algorithms
Normal Computing CorporationResponsibilities
- Develop algorithms for LLM inference workloads running on in-memory hardware and demonstrate improvements through simulation and silicon.
- Co-design software, algorithms, hardware, and architecture with cross-functional teams.
- Design numerical methods that exploit thermal noise, analog dynamics, and the physical behavior of the hardware.
- Build evaluation frameworks and benchmarks for algorithm behavior in simulation and on real hardware.
- Translate LLM workload insights into hardware design constraints and opportunities.
- Prototype and iterate as the hardware progresses from simulation to FPGA prototypes and silicon.
- Optimize performance at both the algorithmic and gate levels.
Requirements
- Deep understanding of LLM inference, including attention mechanisms, KV cache, long-context decoding, and memory-bandwidth constraints.
- Experience with inference optimization such as quantization, sparsity, kernel fusion, or memory-efficient attention.
- Familiarity with stochastic systems, probabilistic methods, numerical analysis, or analog computation.
- Experience implementing algorithms close to hardware beyond high-level frameworks.
- Ability to reason from first principles about the capabilities of novel computing substrates.
- Track record of taking ideas from theory to working implementations on real hardware.
- Strong programming skills in Python and at least one systems language.
- Collaborative ability to work across hardware, architecture, and software teams.
- PhD in machine learning, applied mathematics, physics, electrical engineering, or a related field is a bonus.
- Exposure to analog or mixed-signal systems, in-memory compute, or non-von-Neumann architectures is a bonus.
- Publications or open-source work in efficient inference, stochastic algorithms, or novel computing are bonuses.
Tech Stack
Categories
About Normal Computing Corporation
At Normal, we're rewriting AI foundations to advance the frontier of reasoning and reliability in the physical world. We are tackling problems across semiconductors and industrials with a mix of interdisciplinary approaches across the full stack: from probabilistic software infrastructure and algorithms to hardware and physics, enabling AI that can reason and understand its own limits. We understand that our technology is only as powerful as the people behind it. Every employee drives significant impact within our products, often working directly with customers and embedding across our tightly-knit team. Our team members are driven by curiosity and passion for solving some of the most challenging problems in the world of atoms. Normal was founded in 2022 by engineers and scientists that pioneered industry-leading Physics + ML tools for next-gen AI at Google Brain and Google X.