How to apply for Engineering Manager, Machine Learning

About Overstory

Overstory uses satellite imagery and machine learning to find vegetation risks near power lines, helping utilities prevent wildfires and outages. The company works across the Americas and Europe with utility partners. The work directly addresses wildfire risk and grid reliability, which are concrete climate problems.

About the role

You will manage a machine learning engineering team that builds models to detect and prioritize vegetation risks from satellite data. The role combines people management with technical direction, and your team's output feeds directly into utility decisions that affect wildfire prevention. This is a hands-on leadership role at a company where ML is the core product, not a support function.

A typical day

You might start with a standup with ML engineers, then review a design doc for a new vegetation detection model. Later you could meet with product to prioritize which utility customer risks to address next, and end the day with a one-on-one focused on an engineer's growth. The exact rhythm depends on the team, so ask about meeting cadence and on-call expectations during interviews.

Who Overstory is looking for

• Has managed ML engineers or data scientists, including hiring, performance reviews, and career development.
• Has shipped production ML systems, ideally involving computer vision, geospatial data, or satellite imagery.
• Can set technical direction for model development, data pipelines, and evaluation metrics while still reviewing code and design docs.
• Is comfortable working remotely with a distributed team across US and European time zones.
• Understands or is willing to learn the utility and wildfire risk domain enough to prioritize ML work that matters to customers.

Tips for this application

• In your resume, list specific ML systems you have shipped and the team size you managed for each. Include model types, data scale, and business impact.
• Mention any experience with satellite imagery, remote sensing, geospatial data, or computer vision. If you lack it, describe adjacent experience with large-scale image or sensor data.
• Reference Overstory's product directly: vegetation risk detection for utilities. Explain how your ML leadership would improve detection accuracy or prioritization.
• Since the role is remote across US and Europe, note your experience managing distributed teams and how you handle async communication across time zones.
• Avoid generic climate statements. Instead, connect your ML management experience to a specific problem Overstory solves, like reducing false positives in vegetation risk alerts or scaling model inference over large regions.

What to cover in your cover letter

Explain why you want to manage ML engineers at a company where the models directly reduce wildfire risk. Describe a time you set ML roadmap priorities under uncertainty and how you measured success. Show you understand the difference between managing ML research and managing production ML systems. If you have domain experience in energy, utilities, or geospatial data, make that explicit and tie it to Overstory's use case.

Research before applying

• Read Overstory's product pages and any public case studies about how utilities use their vegetation risk data.
• Look up the founders and leadership team to understand their technical and climate backgrounds.
• Search for public talks, blog posts, or papers from Overstory engineers about their ML stack, satellite data sources, or model evaluation methods.
• Check recent news about wildfires, grid reliability, and utility vegetation management to understand the regulatory and operational context Overstory operates in.

Likely interview topics

Based on the job description, expect questions about:

• How you would structure an ML team to balance model research with production reliability for satellite-based vegetation detection.
• A time you had to deprioritize a technically interesting ML project because it did not serve customer needs.
• How you evaluate and improve model performance when ground truth labels are scarce or expensive, which is common in satellite imagery.
• How you manage performance and career growth for ML engineers who work remotely across multiple time zones.
• Your approach to cross-functional collaboration with product, sales, and utility customers who may not have ML backgrounds.

Common mistakes to avoid

• Applying with a generic engineering manager resume that does not mention ML systems, model deployment, or data pipelines.
• Focusing only on people management without showing you can still engage with technical ML decisions like model selection, evaluation metrics, or data quality.
• Ignoring the domain entirely. Not mentioning utilities, wildfire risk, satellite imagery, or geospatial data suggests you have not read the job description.
• Treating the remote aspect as an afterthought. Overstory spans the Americas and Europe, so not addressing distributed team management is a gap.