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Whisker Labs

Senior ML Platform Engineer

Reposted 4 Days Ago
Remote
Hiring Remotely in United States
Senior level
Remote
Hiring Remotely in United States
Senior level
Design and improve ML training frameworks, modularize experiments, adapt fire-hazard detection algorithms for varied electrical environments, build scalable pipelines for high-frequency sensor data, and develop dashboards and tooling to monitor model performance and drift while collaborating across product, data science, and fire safety teams.
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About Whisker Labs

We’re on a mission to save lives and property, leading the next wave in smart home technology and fire prevention with Ting. This intelligent sensor and concierge service monitors a home’s electrical network to detect electrical hazards that often lead to the most devastating and catastrophic fires. While on the job preventing fires inside a home, Ting also helps monitor the electrical grid, contributing to increased community fire safety and reduced environmental impact that comes with fire reduction. We’re steadfastly addressing the long-underserved realm of electrical fire prevention with leading-edge technology and embarking on the next stage of our growth. Visit tingfire.com for more information.

About the Role:

Whisker Labs is seeking a Senior ML Platform Engineer to join our fully remote Data Science team.

 

As part of the team, you will be responsible for advancing Whisker Labs’ technology to detect early warning signs of electrical fires in homes, preventing one of the deadliest types of fires. The team’s primary focus is on developing algorithms, automation, and internal web-based tools to detect electrical fire hazards and enable our fire safety team to efficiently mitigate them. Whisker Labs is growing rapidly, but the team retains a high-energy, fast moving, creative culture.

 

Our ideal candidate brings an energetic, creative approach to their work and thrives in a dynamic, fast-paced environment. You are excited to improve and scale existing solutions to meet new challenges, ensuring reliability and model performance across production systems. You’re comfortable working across the stack, expanding your skills as you go, and are eager to contribute to a project that will have a substantial, positive impact on people’s lives.

 

Key Responsibilities

  • Drive architectural improvements to our internal ML training frameworks, including modularization and experiment tracking
  • Adapt and optimize existing fire hazard detection algorithms for new electrical environments and grid configurations
  • Build and maintain scalable data pipelines that process high-frequency sensor data and feed downstream systems
  • Develop dashboards and automated tooling to monitor models, identify drift, and own processes to ensure models continue to meet their performance expectations
  • Maintain a deep understanding of our stack, and collaborate across data science, product, and fire safety leadership to conceptualize and deliver improvements


Qualifications

  • A degree in Mathematics, Science, or an Engineering Field
  • 8+ years of combined education and experience working with large-scale data analysis/processing
  • Expert-level fluency in Python
  • Experience with a Linux-based development workflow (e.g., git, SSH, Make, shell scripting)
  • Ability to work effectively in a fully remote, fast-paced environment
  • Independent, self-learner, excellent problem solver


Preferred Qualifications

  • Advanced degree in Computer Science, Electrical Engineering, Data Science, Data Engineering, Machine Learning, or a similar academic field
  • Experience with a data-driven technology stack; building and maintaining ML pipelines, frameworks, or libraries
  • Experience with cloud ecosystems (AWS preferred) and containerized services

What We Offer

  • Competitive salary + equity
  • The ability to make, own, and carry out decisions
  • Health, dental, and vision insurance
  • 401(k) with match


Whisker Labs is an equal opportunity employer. We evaluate all employees and job applicants based on merit and ability to perform the role, without regard to identity or any legally protected class. We are committed to providing an inclusive interview experience for all candidates.

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