Job
- Level
- Experienced
- Job Field
- IT, Data, DevOps
- Employment Type
- Full Time
- Contract Type
- Permanent employment
- Location
- Vienna
- Working Model
- Hybrid, Onsite
Job Summary
You will develop and operate the ML platform for training and on-device deployment, build scalable data/training pipelines, and automate CI/CD, testing, quantization and packaging with Docker, Kubernetes and TensorFlow/PyTorch.
Job Technologies
Your role in the team
- In this role, you will be supporting the development of cutting edge machine learning technologies for the next generation of Spectacles.
- Working from our Vienna office, you will be collaborating with other machine learning, computer vision and software teams of Spectacles teams around the world.
- Own the ML platform to support the training, evaluation and deployment of cutting edge ML models for on-device applications.
- Build data and training pipelines at scale.
- Apply strong software engineering to deliver scalable, reproducible end-to-end ML workflows for deep learning and computer vision.
- Develop optimization and release toolchain for automated testing/validation, CI/CD for ML, quantization/distillation and packaging.
- Drive operational excellence by advocating and applying best practices for scalability and cost management.
- Work together with our cross-functional engineering and research teams in computer vision and machine learning.
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Our expectations of you
Education
- Bachelor's degree in a technical field such as computer science or equivalent experience.
Qualifications
- Excellent software design, development and debugging skills in the context of ML systems.
- Proven track record of developing highly available systems that handle large amounts of data.
- Working knowledge of ML fundamentals.
- Strong communications and interpersonal skills.
- A genuine passion for learning new things and helping colleagues improve.
- Ability to travel as needed.
- Familiarity with Metaflow, Airflow, Kubeflow or similar workflow orchestration framework.
Experience
- 4+ years of relevant industry experience.
- Experience with Python, C++ or equivalent combined with a proven track record of learning on the job.
- Experience with machine learning platforms and infrastructure.
- Experience building large-scale production machine learning systems or data pipelines.
- IMPORTANT RULES: - Keep the original formatting and structure - Translate technical terms correctly (e.g., \"Frontend\", \"Backend\", \"DevOps\", \"Scrum Master\") - Use industry-standard terminology for HR and tech - Maintain the tone (formal/informal, you) - Translate only the text; do not add explanations - In bullet points, preserve the structure - Technical terms that are internationally used may remain untranslated - Ensure correct grammar and natural language flow Text to translate: Experience with Docker, Kubernetes, Istio/Envoy, NoSQL solutions, Memcache/Redis, Google/AWS services.
- IMPORTANT RULES: - Keep the original formatting and structure - Translate technical terms correctly (e.g. "Frontend", "Backend", "DevOps", "Scrum Master") - Use industry-standard terminology for HR and Tech - Maintain the tone (formal/informal, you) - Translate only the text, do not add explanations - Maintain the structure in bullet lists - Technical terms that are internationally used can remain untranslated - Ensure correct grammar and natural language flow Text to translate: Experience with TensorFlow, PyTorch, or related deep learning frameworks.
- Experienced in MLOps: managing production machine learning lifecycle.
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Description
- Company Type
- Established Company
- Working Model
- Hybrid, Onsite
- Industry
- Internet, IT, Telecommunication
Dev Reviews
by devworkplaces.com
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(1 Review)3.6
Workingconditions
4.6Engineering
2.7Career Growth
3.4Culture
4.0
Machine Learning Platform Engineer
- Location
- Vienna
- Working Model
- Hybrid, Onsite
