Senior Machine Learning Engineer
Data
Senior Machine Learning Engineer
Egnyte Poland
Poznań
Type of work
Undetermined
Experience
Senior
Employment Type
B2B
Operating mode
Remote
Egnyte Poland
Egnyte is the secure multi-cloud platform for content security and governance that enables organizations to better protect and collaborate on their most valuable content. Established in 2008, Egnyte has democratized cloud content security for more than 22,000 + organizations, helping customers improve data security, maintain compliance, prevent and detect ransomware threats, and boost employee productivity on any app, any cloud, anywhere. For more information, visit www.egnyte.com.
Tech stack
Python
Big Data
SQL
Docker
Deep Learning
Machine Learning
Kubernetes
Google Cloud Platform
kubeflow
Job description
Online interview
About Egnyte:
Egnyte is a product-focused company, not a software outsourcing business. We build and maintain our flagship software: a secure content platform called Egnyte, used by companies like Red Bull and Yamaha. With 200+ people working in our Poznań office, we remain a people-first workplace.
The opportunity:
We are looking for an experienced engineer who will help us to design, develop, and deploy machine learning models in production. The core of the work will be focused on developing sound and robust models, preparing their deployment in a controlled manner, while providing appropriate means to monitor their performance and stability. Besides tasks associated with developing models into production, an important part of the work concerns the development of appropriate approaches and tools to ensure a professional management of our models in production.
Your day-to-day at Egnyte:
- Preparing datasets needed to train and validate our machine learning models
- Defining and implementing metrics to evaluate the performance of our models, both for computing performance (such as CPU & memory usage) and for ML performance (such as precision, recall, and F1)
- Supporting the deployment of machine learning models on our infrastructure, including containerization, instrumentation, and versioning
- Supporting the whole lifecycle of our machine learning models, including gathering data for retraining, A/B testing, and redeployments
- Developing, testing, and evaluating tools for machine learning models deployment, monitoring, retraining.
- Working closely within a distributed team to analyze and apply innovative solutions over billions of documents
- Supporting solutions ranging from rule-based to deep learning systems.
- Partnering with cross-functional team members to bring large scale data engineering solutions to production
- Communicating your approach and results to a wider audience through presentations
About you:
- Demonstrated success with machine learning in a SaaS or Cloud environment, with hands–on knowledge of model creation and deployments in production at scale
- Good knowledge of traditional machine learning methods and neural networks
- Fluency in at least one deep learning framework: PyTorch, TensorFlow / Keras
- Ability to implement data import, cleansing and transformation functions at scale
- Ability to create models in Python
- Fluency in Docker, Kubernetes
- Working knowledge of relational and dimensional data models
- Solid English skills to effectively communicate with other team members
Bonus skills:
- Experience with large datasets and distributed computing, especially with the Google Cloud Platform
- Good understanding of advanced analytical modeling and statistical forecasting techniques
- Experience with No–SQL and Graph databases
- Knowledge of Java, Scala or Golang programming languages
- Familiarity with Kubeflow
- Experience with the HugginFace or the Fairseq libraries
- Experience with OpenCV
What we can offer you:
- Attractive salary package based on skillset
- Flexible forms of employment and working hours
- 100% remote work possible also after pandemic time
- Stock options
- Your own Egnyte account with lifetime access to 1 TB of cloud storage
- 4000 PLN gross conference budget per person and additional 4 training days off each year
- MultiKafeteria: you can choose a MultiSport card or gift cards every month
- Private medical health care
- In-house English classes