AI Systems Engineer for Time Series Forecasting, Optimization, and LLM-based Coordination (m/f/d) - Early Stage Startup

About the Company

adjusted flow is an early-stage company building a software platform for energy consultants and energy-intensive manufacturers. The product is designed to help organizations use on-site flexibility more effectively across generation and consumption, with the broader mission of accelerating the energy transition in industry. The team is still laying the groundwork for a scalable product, so this role is intended to help shape both the platform and the technical direction from the ground up.

Role Overview

The position focuses on the AI and decision-making core of EOIS for industrial energy optimization. It brings together forecasting, uncertainty handling, optimization integration, and supervised intelligent control for live industrial environments. The work will be hands-on and will influence how the platform behaves in production.

Responsibilities

• Build and refine forecasting models for industrial electricity demand, PV generation, and other energy-related signals.
• Expand deterministic forecasting into probabilistic outputs such as quantiles, confidence measures, and well-calibrated uncertainty ranges.
• Measure forecast performance on industrial datasets that are noisy, incomplete, and subject to changing patterns.
• Shape how forecast results and uncertainty estimates feed into optimization processes, especially for flexible assets such as battery storage.
• Help develop the supervisory intelligence layer that assesses system state, model quality, and service health.
• Define controlled decision logic for actions such as triggering re-forecasting, changing operating modes, suppressing low-confidence results, or handing over to a human reviewer.
• Work on structured AI workflows and production deployment within Python services, gRPC interfaces, and the live EOIS platform.
• Assist with validation, benchmarking, and explaining technical results in real customer settings.

Requirements

• You are expected to be proactive, curious, and willing to build solutions rather than wait for detailed instructions.
• You should be comfortable challenging assumptions and proposing better approaches when needed.
• You should be able to take responsibility even when the scope is still evolving.
• Strong Python experience is expected.
• Experience with time-series forecasting in industrial or energy contexts is important.
• Knowledge of probabilistic forecasting methods such as conformal prediction, quantile regression, ensemble approaches, or probabilistic boosting is needed.
• You should understand calibration and uncertainty evaluation.
• Working knowledge of LP/MILP or similar optimization methods is required.
• Familiarity with optimization tools such as Pyomo, PuLP, or comparable frameworks is useful.
• Experience designing model outputs for downstream decision systems is important.
• LLM application engineering with structured outputs is relevant to the role.
• You should be able to define bounded decision or action spaces.
• Understanding of industrial energy systems, especially PV, BESS, load, and ideally hydro, is valuable.
• The company prefers candidates with at least 2 years of overall work experience.
• At least 1 year of that experience should ideally come from a small company, startup, or industry research lab.
• Practical experience in execution-focused, non-state-funded environments is preferred.
• Backgrounds limited to universities, publicly funded academic research, or very large highly structured corporations are generally not sufficient on their own for this role.
• Applicants are encouraged to include a short note explaining their motivation and highlighting projects where they used the relevant technical skills.
• The ideal start date is June 1.

Benefits

• High responsibility from the beginning.
• Steep learning opportunities and exposure to unfamiliar challenges.
• Direct impact on the product and the decisions behind it.
• Work on a meaningful problem tied to the energy transition in industry.
• Fast collaboration and short decision paths within a close-knit team.
• Potential to grow into a key long-term role in the company.
• Coffee and snacks are available, along with a team open to ideas and suggestions.

Additional Information

The company is still building its foundation, so the environment may not be fully structured yet. That is seen as an opportunity for someone who wants to make a real difference while helping shape the company’s direction. The team is specifically looking for someone who wants to contribute ideas, take responsibility, and improve how things are done.