How to apply for Staff Product Manager, Machine Learning

About Overstory

Overstory uses AI and satellite imagery to help electric utilities find vegetation that threatens power lines. The company's goal is to reduce wildfire risk and improve grid reliability. This is a chance to work on climate problems with a direct link to infrastructure that communities depend on.

About the role

As a staff product manager for machine learning, you will define and drive the ML products that turn satellite data into vegetation risk insights for utilities. You will work at the intersection of remote sensing, ML engineering, and utility customer needs. The role is remote in the US and full-time.

A typical day

A typical day might involve meeting with ML engineers to review model progress, talking with utility customers to understand their risk priorities, and working with design and engineering to plan the next product iteration. You will likely spend time on data quality issues and aligning stakeholders across remote teams.

Who Overstory is looking for

• Has shipped ML-powered products end to end, ideally in geospatial, remote sensing, or similar data-heavy domains.
• Can translate utility customer problems into ML product requirements and work closely with ML engineers and data scientists.
• Understands trade-offs in model performance, data quality, and operational constraints for real-time or near-real-time systems.
• Has experience working with external customers or partners in industries like utilities, energy, or infrastructure.

Tips for this application

• In your resume, highlight specific ML products you have taken from problem definition to launch, including the data and model choices you influenced.
• Mention any experience with satellite imagery, geospatial data, or vegetation management if you have it.
• Show that you understand the utility industry or wildfire risk by referencing concrete examples in your application materials.
• If you have worked remotely across time zones, note how you handled communication and delivery.
• Apply through the company's careers page and check if they ask for a cover letter. If they do, focus on why this problem matters to you and how your ML product experience fits.

What to cover in your cover letter

Explain why you want to work on wildfire risk and grid resilience specifically. Describe an ML product you managed and the outcomes it delivered. Show that you can work with both technical ML teams and utility customers. If you have domain experience in energy or geospatial, make that clear.

Research before applying

• Read Overstory's website and any published case studies to understand how their product is used by utilities.
• Look up the basics of utility vegetation management and wildfire risk to speak the language of the industry.
• Check the backgrounds of the founders and team on LinkedIn to understand their technical and domain strengths.
• Search for recent news about Overstory, such as funding, partnerships, or product launches.

Likely interview topics

Based on the job description, expect questions about:

• How would you prioritize ML product features when model performance, data cost, and customer urgency conflict?
• Walk through an ML product you shipped. What was the problem, what did you build, and how did you measure success?
• How do you work with ML engineers to set expectations around model accuracy and latency?
• What do you know about vegetation management for utilities and how satellite data can help?
• How would you gather requirements from utility customers who may not have ML expertise?

Common mistakes to avoid

• Submitting a generic resume that does not mention ML products, geospatial data, or utility-related work.
• Focusing only on ML theory without showing how you have applied it to real customer problems.
• Ignoring the climate and infrastructure context and treating this as just another product management role.