Lemurian Labs
Senior ML Performance Engineer
Lemurian Labs, San Francisco, California, United States, 94199
At Lemurian Labs, we're on a mission to bring the power of AI to everyone—without leaving a massive environmental footprint. We care deeply about the impact AI has on our society and planet, and we're building a solid foundation for its future, ensuring AI grows sustainably and responsibly. Innovation should help the world, not harm it.
We are building a high-performance, portable compiler that lets developers "build once, deploy anywhere." Yes, anywhere. We're talking about seamless cross-platform compatibility, so you can train your models in the cloud, deploy them to the edge, and everything in between—all while optimizing for resource efficiency and scalability.
The Role Senior ML Performance Engineer to architect and lead our Performance Testing Platform from the ground up. You'll be the technical authority on how we measure, validate, and optimize the performance of large language models (Llama 3.2 70B, DeepSeek, and others) before and after compiler optimization on modern GPU architectures.
What You'll Do
Design and build a comprehensive performance testing platform for evaluating LLM inference workloads across GPU clusters
Define and implement the benchmarking methodology, metrics, and test suites that measure latency, throughput, memory utilization, power consumption, and model accuracy
Establish baseline performance for unoptimized models (Llama 3.2 70B, DeepSeek, etc.) and validate post-optimization improvements
Develop automated testing pipelines for continuous performance validation across compiler releases and model updates
Investigate performance bottlenecks using profiling tools (ROCm profilers, GPU traces, system-level monitoring) and work with the compiler team to drive optimizations
Create dashboards and reporting that provide clear visibility into performance trends, regressions, and wins
Collaborate cross-functionally with compiler engineers, ML engineers, and DevOps to ensure performance testing is integrated into our development workflow
Document best practices for performance testing and optimization of ML workloads on GPU hardware
What You'll Bring
7+ years of experience in performance engineering, benchmarking, or systems engineering roles
Deep understanding of ML inference workloads, particularly transformer-based models and LLMs
Hands‑on experience with GPU programming and optimization (CUDA, ROCm, or similar)
Strong programming skills in Python and C/C++
Proven track record of building performance testing infrastructure or benchmarking platforms from scratch
Experience with ML frameworks (PyTorch, TensorFlow, ONNX Runtime, vLLM, TensorRT‑LLM, etc.)
Proficiency with profiling and debugging tools for GPU workloads
Strong analytical skills with the ability to design experiments, analyze results, and communicate findings clearly
Experience with CI/CD systems and test automation frameworks
Nice to Have
Experience with AMD GPUs (Mi200/Mi300 series) and ROCm ecosystem
Knowledge of compiler optimization techniques and their impact on performance
Experience with distributed inference and multi‑GPU workloads
Familiarity with ML model quantization, pruning, and other optimization techniques
Background in high-performance computing or systems-level optimization
Experience with infrastructure‑as‑code (Kubernetes, Docker, Terraform)
Contributions to open-source ML or systems projects
Personal Attributes
Obsessive about details
— you notice the 2% regression that others miss
Self-driven
— you take ownership and don't wait for permission to solve problems
Collaborative mindset
— you work well across teams and help others succeed
Passionate about sustainability
— you care about making AI more efficient and environmentally responsible
Clear communicator
— you can explain complex technical concepts to both engineers and stakeholders
Salary depends on experience and geographical location. This salary range may be inclusive of several career levels and will be narrowed during the interview process based on a number of factors, such as the candidate’s experience, knowledge, skills, and abilities, as well as internal equity among our team.
Additional benefits for this role may include: equity, company bonus opportunities, medical, dental, and vision benefits; retirement savings plan; and supplemental wellness benefits.
Lemurian Labs ensures equal employment opportunity without discrimination or harassment based on race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity or expression, age, disability, national origin, marital or domestic/civil partnership status, genetic information, citizenship status, veteran status, or any other characteristic protected by law.
EOE
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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We are building a high-performance, portable compiler that lets developers "build once, deploy anywhere." Yes, anywhere. We're talking about seamless cross-platform compatibility, so you can train your models in the cloud, deploy them to the edge, and everything in between—all while optimizing for resource efficiency and scalability.
The Role Senior ML Performance Engineer to architect and lead our Performance Testing Platform from the ground up. You'll be the technical authority on how we measure, validate, and optimize the performance of large language models (Llama 3.2 70B, DeepSeek, and others) before and after compiler optimization on modern GPU architectures.
What You'll Do
Design and build a comprehensive performance testing platform for evaluating LLM inference workloads across GPU clusters
Define and implement the benchmarking methodology, metrics, and test suites that measure latency, throughput, memory utilization, power consumption, and model accuracy
Establish baseline performance for unoptimized models (Llama 3.2 70B, DeepSeek, etc.) and validate post-optimization improvements
Develop automated testing pipelines for continuous performance validation across compiler releases and model updates
Investigate performance bottlenecks using profiling tools (ROCm profilers, GPU traces, system-level monitoring) and work with the compiler team to drive optimizations
Create dashboards and reporting that provide clear visibility into performance trends, regressions, and wins
Collaborate cross-functionally with compiler engineers, ML engineers, and DevOps to ensure performance testing is integrated into our development workflow
Document best practices for performance testing and optimization of ML workloads on GPU hardware
What You'll Bring
7+ years of experience in performance engineering, benchmarking, or systems engineering roles
Deep understanding of ML inference workloads, particularly transformer-based models and LLMs
Hands‑on experience with GPU programming and optimization (CUDA, ROCm, or similar)
Strong programming skills in Python and C/C++
Proven track record of building performance testing infrastructure or benchmarking platforms from scratch
Experience with ML frameworks (PyTorch, TensorFlow, ONNX Runtime, vLLM, TensorRT‑LLM, etc.)
Proficiency with profiling and debugging tools for GPU workloads
Strong analytical skills with the ability to design experiments, analyze results, and communicate findings clearly
Experience with CI/CD systems and test automation frameworks
Nice to Have
Experience with AMD GPUs (Mi200/Mi300 series) and ROCm ecosystem
Knowledge of compiler optimization techniques and their impact on performance
Experience with distributed inference and multi‑GPU workloads
Familiarity with ML model quantization, pruning, and other optimization techniques
Background in high-performance computing or systems-level optimization
Experience with infrastructure‑as‑code (Kubernetes, Docker, Terraform)
Contributions to open-source ML or systems projects
Personal Attributes
Obsessive about details
— you notice the 2% regression that others miss
Self-driven
— you take ownership and don't wait for permission to solve problems
Collaborative mindset
— you work well across teams and help others succeed
Passionate about sustainability
— you care about making AI more efficient and environmentally responsible
Clear communicator
— you can explain complex technical concepts to both engineers and stakeholders
Salary depends on experience and geographical location. This salary range may be inclusive of several career levels and will be narrowed during the interview process based on a number of factors, such as the candidate’s experience, knowledge, skills, and abilities, as well as internal equity among our team.
Additional benefits for this role may include: equity, company bonus opportunities, medical, dental, and vision benefits; retirement savings plan; and supplemental wellness benefits.
Lemurian Labs ensures equal employment opportunity without discrimination or harassment based on race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity or expression, age, disability, national origin, marital or domestic/civil partnership status, genetic information, citizenship status, veteran status, or any other characteristic protected by law.
EOE
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
#J-18808-Ljbffr