Location: Remote or Hybrid
Employment Type: Full-time
About Valuematic
We specialize in intelligent autoscaling solutions, leveraging ML-driven time series forecasting and optimization algorithms to predict and manage cloud resource usage dynamically.
About the Role
We are looking for a Data Scientist / ML Engineer with expertise in time series forecasting, optimization algorithms, and ML model deployment.
You’ll work on developing predictive models that anticipate cloud workload demands and optimize resource allocation.
Your Responsibilities:
- Develop time series forecasting models for autoscaling predictions
- Build algorithms for dynamic resource allocation
- Design and deploy scalable ML pipelines for real-time cloud management
- Implement MLOps strategies for continuous model monitoring and retraining
- Work with large-scale data streams to drive automation and insights
What You Bring
- Expertise in time series modeling and forecasting techniques
- Strong Python skills with experience in ML frameworks
- Experience in reinforcement learning and optimization methods
- Familiarity with containerization and cloud-native ML deployment
- Knowledge of real-time data processing and streaming architectures
- Competitive salary and performance-based incentives
- Remote-first work environment with flexible hours
- Opportunity to work on technically challenging problems in cloud infrastructure
- Direct impact on product direction and technical strategy
- A fast-paced, no-bureaucracy startup culture
Why Join Valuematic?
- Build AI-driven autoscaling solutions for global cloud providers
- Work on real-time, high-impact optimization challenges
- Be part of an innovative start-up defining the future of cloud computing
Send us your GitHub/GitLab profile or a few notes about what projects you’ve built or managed.
We care more about hands-on experience and problem-solving than perfect resumes.
Contact
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