Research Engineer, Algorithms

Normal Computing

. Develop algorithms for LLM inference workloads running on in-memory hardware .

Posted 10/22/2026full-timeMountain View • California • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing algorithms for large model inference workloads, optimizing performance at both gate and algorithmic levels, and collaborating effectively across hardware and software teams. Proficient in implementing algorithms close to hardware and validating them against various prototypes.

Highest-signal resume keywords

Large Model InferenceInference OptimizationProgramming Skills in PythonNumerical AnalysisHardware and Software Co-Design

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Algorithm DevelopmentInference Optimization TechniquesNumerical Methods DesignPerformance OptimizationAlgorithm ValidationStochastic Systems FamiliarityProbabilistic MethodsFirst Principles ReasoningImplementation on Real HardwareAttention Mechanisms

Soft Skills

Collaboration Across TeamsProblem-Solving

Tools & Technologies

FPGA PrototypesSimulation FrameworksIn-Memory Hardware

Certifications & Qualifications

PhD in Machine LearningPhD in Applied MathematicsPhD in PhysicsPhD in Electrical Engineering

Industry Keywords

Analog ComputationMixed-Signal SystemsNon-Von-Neumann ArchitecturesThermal Noise ExploitationMemory Bandwidth Constraints

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Develop algorithms for LLM inference workloads running on in-memory hardware
Work directly with hardware and architecture teams on software/hardware co-design
Design numerical methods that exploit thermal noise and analog dynamics
Build evaluation frameworks and benchmarks for algorithms in simulation and on real hardware
Translate model-workload insights into hardware design constraints and opportunities
Prototype and iterate as hardware evolves from simulation to silicon
Optimize performance at both gate and algorithmic levels
Validate algorithms against simulators, FPGA prototypes, and silicon as it becomes available

Requirements

What you’ll need

Deep understanding of large model 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 novel computing substrates
Track record of taking ideas from theory to working implementation on real hardware
Strong programming skills in Python and at least one systems language
Ability to work across hardware, architecture, and software teams
PhD in machine learning, applied mathematics, physics, electrical engineering, or a related field (bonus)
Exposure to analog or mixed-signal systems, in-memory compute, or non-von-Neumann architectures (bonus)
Experience working on hardware that did not yet exist when you joined (bonus)
Publications or open-source work in efficient inference, stochastic algorithms, or novel computing (bonus)

Benefits

Comp & perks

Inclusive environment committed to diversity
Reasonable disability accommodations
Equal employment opportunity protections