Founding Engineer at Clera-AI

Munich, Bavaria, Germany -

Full Time

Start Date

Immediate

Expiry Date

16 Oct, 26

Salary

95000.0

Posted On

18 Jul, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, AWS, Cloud Infrastructure, Electricity Market Integration, Backtesting Environments, Machine Learning, Time-series Forecasting, MILP, Stochastic Methods, RNNs, Reinforcement Learning, Asset Steering, High-frequency Trading, Fault Tolerance, Scalability, Production Code

Industry

technology;Information and Internet

Description

About the Role A fast-growing pre-seed cleantech startup in Munich is hiring its first engineering hire — a Founding Engineer who will work directly alongside the CTO to build and operate live systems that steer industrial energy assets and trade on short-term electricity markets. This is a highly cross-functional, end-to-end role sitting at the intersection of cloud infrastructure, electricity market integrations, and machine learning. You will be central to delivering reliable 24/7 operations for customers in the industrial electrification space — helping heavy industry transition away from fossil fuels by leveraging AI-driven Power-to-Heat optimization and smart electricity market participation. The company operates in the industrial energy / cleantech sector and uses proprietary AI algorithms to forecast revenue opportunities and automate cross-market trading strategies for Power-to-Heat assets, reducing both costs and CO₂ emissions for industrial clients. What You'll Do Operate and maintain a reliable cloud infrastructure enabling 24/7 asset steering and high-frequency trading with high availability, fault tolerance, and scalability. Develop and improve interfaces to electricity markets (Day-ahead, Intraday, Control power) and hardware steering systems. Expand and maintain a backtesting environment to support customer configurations, new trading algorithms, and new asset types. Train, deploy, and monitor electricity trading models in production. Within your first quarter: build a new asset type into the backtesting environment and design the cloud architecture for live operations. Rapidly internalize customer needs and translate them into robust, production-grade technical solutions. What We're Looking For Required: Strong fundamentals in computer science, mathematics, or physics — ideally from a top university background. Proven track record shipping production code; hands-on experience with cloud infrastructure (ideally AWS) and Python. Experience designing and operating cloud systems for 24/7 live operations. Hands-on experience building or maintaining backtesting environments for trading algorithms and varied asset types. Familiarity with electricity markets and the ability to design reliable market interfaces. Ability to own workstreams end-to-end, deliver high-quality results independently, and escalate issues early. Fluent in English; based in Munich or willing to work on-site in Munich most of the time. Nice to Have: Optimization and data-science experience: MILP, stochastic methods, RNNs, Reinforcement Learning. Time-series forecasting in production environments. Energy / climate-tech domain knowledge. Fluent German. The right mindset is valued over ticking every box — we encourage you to apply even if you don't meet every listed qualification. Location On-site in Munich, Germany (most of the time). Visa sponsorship is not available — candidates must have the right to work in Germany. Compensation & Benefits Salary: $70,000 – $95,000 per year (USD equivalent). Founding team equity participation typical of a pre-seed engineering hire. High-impact role with direct influence over technical architecture and product direction from day one. Work on meaningful problems at the frontier of industrial decarbonization and energy markets.

Responsibilities

Build and operate cloud infrastructure for 24/7 industrial energy asset steering and high-frequency electricity trading. Develop market interfaces and maintain backtesting environments to support AI-driven Power-to-Heat optimization.