AI/ML Engineer

As an AI/ML Engineer at Powerline, your main responsibilities include researching, designing, and developing AI/ML algorithms for various business-related problems. You'll also be tasked with building scalable AI solutions and collaborating with cross-functional teams to ensure seamless integration across the tech stack. Additionally, you'll lead efforts to improve model performance continuously and stay updated on the latest trends in AI and renewable energy.

To qualify for the AI/ML Engineer position at Powerline, you’ll need a minimum of 5 years of experience in building AI/ML models, along with 2 years of technical leadership experience in the field. Proficiency in Python and experience with AI/ML libraries like Pytorch, Jax, or TensorFlow are essential. A strong understanding of working with sequential data for modeling and analytics is highly advisable, alongside strong communication skills for effective collaboration.

For the AI/ML Engineer role at Powerline, having experience in energy markets, renewable technologies, or energy storage systems is considered a strong plus. Additionally, familiarity with NLP, LLMs, and agentic systems will give you an edge. If you have expertise in physically informed neural networks, physical loss functions, or timeseries forecasting, especially within energy markets, we’d be very interested in your background.

While not strictly required, experience working in a startup environment is a valuable asset for the AI/ML Engineer position at Powerline. This experience can help you adapt more quickly to our dynamic and fast-paced culture, enabling you to be more effective in contributing to our rapidly evolving projects in the climate-tech space.

A typical day for an AI/ML Engineer at Powerline involves collaborating with your engineering team to brainstorm and design new AI solutions. You’ll spend time coding, developing and testing AI models, analyzing their performance, and continuously iterating to improve them. Frequent communication with cross-functional teams ensures that the technical solutions align well with operational needs, making your role essential to the team's success.

In responding to this question, detail specific AI/ML algorithms you've worked with, such as classification, regression, or time series forecasting, and describe projects where you applied them. Be specific about the challenges you faced and how you overcame them, showcasing your problem-solving skills.

When discussing scalability, emphasize the importance of designing models that are efficient and can process large data sets without performance degradation. Mention how you leverage tools and techniques such as microservices, containerization, or cloud solutions to achieve scalability effectively.

To enhance model performance, highlight strategies like hyperparameter tuning, feature engineering, and utilizing advanced techniques such as ensemble methods. Provide examples indicating how these strategies led to measurable improvements in previous projects.

Discuss your habits for keeping informed, such as following relevant journals, participating in webinars, or engaging with online communities. Mention how you apply new insights to improve your work at Powerline, aligning with the company’s goals.

In your answer, detail the project goals, your role, challenges faced, and the quantitative outcomes achieved. Emphasize your leadership abilities and how you inspired teamwork and innovation in overcoming the project hurdles.

Articulate your experience in MLOps, detailing practices around model deployment, monitoring, and maintenance cycles. Discuss tools and frameworks you've used and how they enhanced the robustness of AI solutions during their lifecycle.

Highlight your communication skills and your appreciation for collaboration. Explain how you ensure that all team members are aligned with the project's goals and how you bridge gaps between technical and non-technical stakeholders.

Share a specific example where you diagnosed an issue, what steps you took to address it, and how you validated the solution. Highlight your analytical skills and ability to learn from the experience.

Explain different techniques you use for data cleaning, normalization, or transformation. Provide examples of how careful preprocessing impacted the performance of your ML models.

Present thoughtful insights regarding emerging trends in AI applications within the energy sector, linking it back to Powerline’s mission. Discuss how AI could drive efficiency and innovation in renewable energy, while emphasizing the importance of sustainability.