TL;DR
Imubit is looking for a top-notch Machine Learning Research Engineer to take deep reinforcement learning algorithms where they have never gone before!
About us:
Imubit directly controls and optimizes refineries and chemical plants with AI to add millions of dollars to the plant bottom line while managing safe operating limits, energy efficiency, and sustainability objectives. Imubit’s Closed Loop Neural Network platform allows customers to leverage Reinforcement Learning (RL) developed by world-class machine learning scientists. Through our patented approach to apply RL for industrial processes, industry leaders have been able to fundamentally change the way they optimize their plants and improve profitability in real-time. Imubit’s solution is currently optimizing the manufacturing facilities of Fortune-500 companies. Imubit has combined the industry expertise from companies like Exxon and Shell with award-winning data scientists endorsed by Google. Imubit is backed by tier-1 venture capital firms such as Insight Partners.
We are looking for:
We are looking for a passionate and experienced Machine Learning Research Engineer with a deep curiosity for understanding systems and a relentless drive to innovate. This role is perfect for a self-starter who thrives on tackling complex problems, exploring new ideas, and pushing boundaries. We want someone who doesn’t just solve given tasks but proactively envisions and develops new applications, challenges the status quo, and enhances models and methodologies to deliver cutting-edge solutions.
As a Machine Learning Engineer in our algo R&D, you will:
- Conduct cutting-edge research to develop new functionalities for our models, expanding Imubit’s core deep reinforcement learning technology.
- Advance the development of AutoML solutions to streamline and enhance machine learning workflows.
- Drive the productization of your research, transforming innovative concepts into impactful, production-ready solutions.
- Design and implement solutions with a focus on scalability, efficient runtime performance, and seamless deployment in production environments.
Requirements:
- MSc in machine learning or related area
- Education in machine learning at the level of advanced academic courses
- At least 4 years of hands-on machine learning experience
- Practical experience with deep learning
- Self-reliant, proactive, with strong motivation and an inquisitive mind
Advantage:
- Experience with deep learning on time series data (Transformers / recurrent models)
- Experience with training neural networks in cloud environments (GCP/AWS) and distributed systems
- Experience/education in signal processing/ control theory/ reinforcement learning
- Academic research experience
- Experience in leading projects and mentoring