At Gauss Labs we are looking for a passionate Machine Learning Engineer who will collaborate with scientists and engineers to improve manufacturing efficiency and reliability in IC manufacturing using data and artificial intelligence.
This role is responsible for implementing batch and online learning capabilities in our machine learning infrastructure.
It is also responsible for capturing the lineage of our ML models as well as measuring and monitoring their performance.
Responsibilities
Design, build, and maintain high availability, multi-tenant training, and inference system for many different content types, such as images and other sensor data
Work closely with data scientists, micro-service developers, and data engineers in our Applied Research and Product Organizations to ensure efficient transfer of scalable solutions
Implement seamless tracking of the lineages of data sets, transformations, feature sets, and hyper-parameters used during ML model training and inference
Enable online learning and automated, continuous delivery of ML models
Implement and deploy automated auditing systems to identify bias and other potential degradations of model performance
Adapt and implement AI models to support real-world use-cases and satisfy production requirements as needed
Basic Qualifications
MS or Ph.
D.
in quantitative fields (CS, Statistics, Math, or Engineering)
7+ years of work experience as MLE, Data Scientist, or related job function
Strong understanding of ML fundamentals and scaling methodologies (e.
g.
, ability to implement an ML algorithm mathematical formulations in an efficient manner w.
r.
t.
scalability, optimization and so on; understanding of ML loss functions, evaluation/validation methods, statistical testing and so on)
Strong communication skills both written and verbally
You are excited to learn, explore new problem areas, and apply your creativity to some of the most challenging and rewarding problems
Familiarity with all aspects of the model development lifecycle
Experience working with cloud products (AWS, GCP, or Azure)
Proficiency in Python, and experience in C++
Prior experience with MLFlow
Knowledge of at least one build tooling system
Track record of writing robust, readable, well documented, well tested, high-performance code.
Experience with containerization, automated eval/testing, CI/CD, MLOps, and cloud services is a plus
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