Senior Research Engineer
About the Role
We are seeking a highly skilled Senior Research Engineer to collaborate closely with both Research and Engineering teams. The role involves diagnosing and resolving bottlenecks across large-scale distributed training, data processing, and inference systems, while also driving optimizations for existing high-performance pipelines.
The ideal candidate possesses a deep understanding of modern deep learning systems, combined with strong engineering expertise in areas such as layer-level optimization, large-scale distributed training, streaming, low-latency and asynchronous inference, inference compilers, and advanced parallelization techniques.
This is a cross-functional role requiring strong technical rigor, attention to detail, intellectual curiosity, and excellent communication skills. The position is embedded within the Research team and is responsible for developing and refining the technical foundation that enables cutting-edge research and translates its outcomes into production, bridging research and production engineering.
Responsibilities
Investigate and mitigate performance bottlenecks in large-scale distributed training and inference systems.
Develop and implement both low-level (operator/kernel) and high-level (system/architecture) optimization strategies.
Translate research models and prototypes into highly optimized, production-ready inference systems.
Explore and integrate inference compilers such as TensorRT, ONNX Runtime, AWS Neuron and Inferentia, or similar technologies.
Design, test, and deploy scalable solutions for parallel and distributed workloads on heterogeneous hardware.
Facilitate knowledge transfer and bidirectional support between Research and Engineering teams, ensuring alignment of priorities and solutions.
Qualifications
Strong expertise in the Python ecosystem and major ML frameworks (PyTorch, JAX).
Experience with lower-level programming (C++ or Rust preferred).
Deep understanding of GPU acceleration (CUDA, profiling, kernel-level optimization); TPU experience is a strong plus.
Proven ability to accelerate deep learning workloads using compiler frameworks, graph optimizations, and parallelization strategies.
Solid understanding of the deep learning lifecycle: model design, large-scale training, data processing pipelines, and inference deployment.
Strong debugging, profiling, and optimization skills in large-scale distributed environments.
Excellent communication and collaboration skills, with the ability to clearly prioritize and articulate impact-driven technical solutions.
Pay Transparency:
AssemblyAI strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity for our team. Our salary ranges are based on paying competitively for our size, stage, and industry, and are one part of many compensation, benefit, and other reward opportunities we provide.
There are many factors that go into salary determinations, including relevant experience, skill level, qualifications assessed during the interview process, and maintaining internal equity with peers on the team. The range shared below is a general expectation for the function as posted, but we are also open to considering candidates who may be more or less experienced than outlined in the job description. In this case, we will communicate any updates in the expected salary range.
The provided range is the expected salary for candidates in the U.S. Outside of those regions, there may be a change in the range which will be communicated to candidates throughout the interview process.
Salary range: $240,000 - $275,000
About the job
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Senior Research Engineer
About the Role
We are seeking a highly skilled Senior Research Engineer to collaborate closely with both Research and Engineering teams. The role involves diagnosing and resolving bottlenecks across large-scale distributed training, data processing, and inference systems, while also driving optimizations for existing high-performance pipelines.
The ideal candidate possesses a deep understanding of modern deep learning systems, combined with strong engineering expertise in areas such as layer-level optimization, large-scale distributed training, streaming, low-latency and asynchronous inference, inference compilers, and advanced parallelization techniques.
This is a cross-functional role requiring strong technical rigor, attention to detail, intellectual curiosity, and excellent communication skills. The position is embedded within the Research team and is responsible for developing and refining the technical foundation that enables cutting-edge research and translates its outcomes into production, bridging research and production engineering.
Responsibilities
Investigate and mitigate performance bottlenecks in large-scale distributed training and inference systems.
Develop and implement both low-level (operator/kernel) and high-level (system/architecture) optimization strategies.
Translate research models and prototypes into highly optimized, production-ready inference systems.
Explore and integrate inference compilers such as TensorRT, ONNX Runtime, AWS Neuron and Inferentia, or similar technologies.
Design, test, and deploy scalable solutions for parallel and distributed workloads on heterogeneous hardware.
Facilitate knowledge transfer and bidirectional support between Research and Engineering teams, ensuring alignment of priorities and solutions.
Qualifications
Strong expertise in the Python ecosystem and major ML frameworks (PyTorch, JAX).
Experience with lower-level programming (C++ or Rust preferred).
Deep understanding of GPU acceleration (CUDA, profiling, kernel-level optimization); TPU experience is a strong plus.
Proven ability to accelerate deep learning workloads using compiler frameworks, graph optimizations, and parallelization strategies.
Solid understanding of the deep learning lifecycle: model design, large-scale training, data processing pipelines, and inference deployment.
Strong debugging, profiling, and optimization skills in large-scale distributed environments.
Excellent communication and collaboration skills, with the ability to clearly prioritize and articulate impact-driven technical solutions.
Pay Transparency:
AssemblyAI strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity for our team. Our salary ranges are based on paying competitively for our size, stage, and industry, and are one part of many compensation, benefit, and other reward opportunities we provide.
There are many factors that go into salary determinations, including relevant experience, skill level, qualifications assessed during the interview process, and maintaining internal equity with peers on the team. The range shared below is a general expectation for the function as posted, but we are also open to considering candidates who may be more or less experienced than outlined in the job description. In this case, we will communicate any updates in the expected salary range.
The provided range is the expected salary for candidates in the U.S. Outside of those regions, there may be a change in the range which will be communicated to candidates throughout the interview process.
Salary range: $240,000 - $275,000