About Sentry
Bad software is everywhere, and we’re tired of it. Sentry is on a mission to help developers write better software faster, so we can get back to enjoying technology.
With more than $217 million in funding and 100,000+ organizations that believe we’re on to something, we’re building performance and error monitoring tools that help companies like Disney, Microsoft, and Atlassian spend less time fixing bugs and more time building products. If you like to selfishly build things that make your digital life better, come help us build the next generation of software monitoring tools.
About The Role
As a Senior Machine Learning Systems Engineer on Sentry’s AI/ML team, you’ll be directly responsible for building the core infrastructure used to develop, evaluate, deploy, iterate on models and pipelines at scale. This role is crucial; you will be at the forefront of integrating machine learning into our core products, from error classification to predictive analytics for application performance monitoring. Your work will help companies around the globe gain actionable insights into their software, enabling them to build better products, faster.
In this role you will
- Be our first Machine Learning Systems Engineer, with an opportunity to own the deployment of major initiatives
- Develop the framework used by Sentry to do time-series analysis at massive scale
- Optimize the performance of transformer-based embeddings models used for critical AI/ML pipelines
You’ll love this job if you
- Are driven by impact and enjoy working on high-stakes, high-visibility projects
- Enjoy building things. You will have the opportunity to build an AI/ML platform at Sentry
- Thrive in cross-functional teams and enjoy building features alongside developers and product teams
- Appreciate the challenges and opportunities that come with scaling machine learning models for real-world applications
Qualifications
- Minimum 7+ years of professional experience with a Bachelor’s degree in computer science, machine learning, or a related field
- You are comfortable writing production quality code (we use Python)
- Experience building and operating large scale distributed systems on a cloud platform like GCP, AWS, Azure (we use Kubernetes + Terraform)
- Experience with distributed large-scale data processing tools, such as Apache Spark or Flink
- Experience with OLAP databases, ideally in the context of time-series data
- Familiarity with machine learning frameworks such as PyTorch or Tensorflow
- Expertise in deploying machine learning models at scale in production environments
- Experience in writing technical documentation, mentoring, and presenting to technical audiences
- Proven track record of owning a system, feature, or component, leading or collaborating with multiple engineers and teams
- Hybrid work schedule for in-person collaboration on Tuesdays, Wednesdays, and Thursdays. For roles considered remote, this will not apply.
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