Reinforcement Learning Specialist
Lausanne, Vaud, Switzerland
INAIT is an AI scale-up founded in 2017 by Henry Markram, renowned professor in neuroscience, director of EPFL’s Blue Brain Project (BBP) and co-founder of Frontiers. INAIT has raised CHF 55 million to fulfill its mission: build a new dimension of AI reverse engineering the way the brain works.
We offer B2B and B2C products and solutions in computer vision, forecasting and document understanding across industries: automotive, healthcare, manufacturing, trading and scientific publishing.
We are a team of 50+ ambitious & creative talents and are looking for an impact-driven research scientist with focus on (Deep) Reinforcement Learning to join the INAIT Forecast Team. The INAIT Forecasting team is developing exciting use cases for financial and non-financial applications.
Your responsibilities
- Apply state of the art machine learning and deep learning techniques to automatically learn strategies based on streams of data
- Conduct and apply RL research for actionizing timeseries forecasts (e.g., automatic trading, asset optimization, preventive maintenance, ...)
- Turn business and decision problems into games (aka environments) that could be solved by reinforcement learning algorithms or control algorithms
- Conduct and apply RL and control theory research to solve those games
- Contribute production level code to INAIT software stack
- Provide detailed analysis and present results in a clear manner
Requirements
Essential experience required
- Experience in solving complex tasks (in difficulty and scale) using (deep) reinforcement learning
- Developing and optimizing machine learning models in python using frameworks such as PyTorch, TensorFlow, scikit-learn, ...
- Familiar with git command line and knowledge of the software development life-cycle (continuous integration, version control, debugging and documentation).
Essential skills
- Robust analytical, mathematical, statistical, and probability skills
- Strong problem-solving skills with an emphasis on product development
- Strong drive to learn and master new technologies and techniques
- Knowledge in game theory and multi-agent machine learning is a plus
- Excellent communication and presentation skills
- Proven ability to work both independently and in team-based environments
Preferred skills
- Established publication record at well-known machine learning conferences
- PhD in mathematics, engineering, computer science or equivalent with 3+ years of experience in machine learning
- Professional experience in applying RL to forecasting use cases
- Professional experience in time series forecasting
- Professional experience in commodity trading / finance
- Previous experience in deploying machine learning algorithms
Recruitment process
- 30-mn call with our HR manager
- 60-mn python interview
- 60-mn ml/dl/reinforcement learning interview
- 60-mn interview with the team
- 30-mn meet the CEO and CTO
What we offer
- Hybrid working model.
- Dynamic work environment, a multicultural and highly talented team in a growing scale-up.
- Attractive salary and compensation scheme including a company participation package.
- Well-located office in Lausanne with easy access by public transportation.
- Free seasonal fresh fruits and drinks.
- Discounts for some products and services in Switzerland
Tags: Computer Science Computer Vision Deep Learning Engineering Finance Git Machine Learning Mathematics ML models PhD Python PyTorch Research Scikit-learn Statistics TensorFlow
Perks/benefits: Career development Conferences
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