Senior Machine Learning Ops @
3 days ago
- 7+ years of experience in a similar role and a Bachelor's degree in Computer Science or a related field.
- Extensive expertise in Python programming and software engineering best practices.
- Demonstrated proficiency with machine learning frameworks such as TensorFlow, Keras, or PyTorch.
- Familiarity with basic SQL for querying and data manipulation.
- Hands-on knowledge of ML pipelines, particularly Kubeflow Pipelines.
- Basic understanding of machine learning techniques sufficient to comprehend and utilize models developed by data scientists. Advanced modeling skills are not required.
- Experience with cloud platforms and their machine learning capabilities, particularly Google Cloud Platform's Vertex AI, is highly desirable.
- Excellent communication skills in both Polish and English.
- A willingness to learn new tools and create proof-of-concept implementations.
- Strong communication skills and the ability to collaborate effectively with team members from diverse backgrounds.
OpenX is seeking a talented Machine Learning Engineer with strong software engineering skills to contribute to impactful R&D projects, with a specific focus on building and maintaining machine learning pipelines. This role centers on engineering solutions, collaborating with the OpenX Data Science team to optimize and advance the ML platform, by providing robust infrastructure and support.
Who We're Looking For:
Thinks long-term and seeks sustainable, robust solutions rather than temporary fixes.
Adheres to industry best practices to ensure the delivery of high-quality results.
Can take an existing machine learning model and productionize it by creating a scalable pipeline for training and optimizing the model for serving.
Will assist in migrating model training pipelines across different technologies or transitioning model implementations to other frameworks.
Provides strategic guidance for optimizing models and enhancing model training pipelines.
Designs, develops, and delivers innovative features and functionalities.
Can effectively communicate with data scientists who are developing and experimenting with models, software engineers integrating those models into broader systems, and DevOps engineers managing serving and infrastructure, facilitating collaboration to build and deploy comprehensive end-to-end solutions.
We are operating at a scale:
100% Cloud-based (GCP) platform
Over 400 billion Ad requests every day
Over 120 000 CPU daily
Over 140 TB RAM daily
Over 50 PB of data per week
Over 1200 production deployments a month
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