Build and maintain machine learning infrastructure for autonomy teams, including model pipelines, observability, inference serving, and compiler platforms. The role requires a relevant degree, at least one year of experience, strong Python skills, and familiarity with C++.
160k – 241k/yr
On-site1+ YOEML Engineering
About the role
Responsibilities
Design and develop machine learning workflow pipelines to train, optimize, validate, and deploy autonomy models.
Develop and maintain continuous testing and monitoring systems for core machine learning infrastructure components.
Build observability to track machine learning model lifecycles from data generation through on-road validation.
Maintain an in-house machine learning inference platform to serve large language models efficiently.
Maintain an in-house machine learning compiler platform to compile, deploy, and validate autonomy models across hardware platforms.
Requirements
Bachelor's, master's, or Ph.D. degree.
1+ years of relevant experience.
Strong proficiency in Python or similar programming languages.
Familiarity with C++.
Ability to develop software with high standards for performance, scalability, and code quality.
Experience working with machine learning pipelines.
Ability to understand and design complex distributed systems.
Ability and willingness to learn quickly, work independently, and improve complex systems.
Nice-to-Haves
Strong proficiency in C++ or other high-performance, low-level languages.
Experience working with large-scale distributed systems.
Experience with system and framework design.
Experience with data workflow orchestration platforms.
Experience with machine learning compilers.
Compensation and Benefits
Base pay range: $160,360–$240,540.
Eligible for an annual performance bonus, equity, and a competitive benefits package.
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