Senior Software Engineer, ML Infrastructure

About the Role:

Planet’s Built Environment applied machine learning team delivers advanced geospatial products primarily for external customers, with a focus on advanced analytics such as change detection, object detection, and emerging generative AI capabilities. While the team applies a range of ML techniques—often leveraging computer vision for remote sensing—this position emphasizes the engineering and MLOps side of our operations: designing and refining workflows to make model deployment, registration, and versioning easy, testable, and scalable. You’ll drive the creation of robust CI/CD pipelines and MLOps workflows that enable quick rollout of new models and iterative improvements to existing ones. By integrating these solutions into our distributed compute platform and ensuring proper monitoring, you’ll help deliver high-impact, production-ready systems. This role will appeal to candidates who have a passion for continuous improvement, automation, and guiding the full product lifecycle—from ideas to well-monitored production systems.

This is a full-time, remote position based in the United States.

Impact You'll Own:

• Building and managing MLOps workflows (monitoring, retraining, benchmarking, and deploying production models and data services)
• Integrating ML models—including those from external partners—into our compute platform and container orchestration environment, ensuring robust operations and support
• Maintaining DevOps tooling (infrastructure as code, CI/CD pipelines, container orchestration)
• Ensuring reliability, scalability, and security of geospatial ML products used by both internal stakeholders and external customers
• Collaborating with adjacent ML and software engineering teams  to ensure seamless integration of ML pre-processing and inference steps, defining best practices for efficient deployment and maintenance of geospatial models

What You Bring:

• 10+ years of software engineering experience of which 5+ years is experience developing production-scale systems (including containerized applications, robust data pipelines, or other ML focused architectures)
• Background in DevOps/MLOps and experience with containerization (Docker), orchestration (Kubernetes), and CI/CD automation
• Fluency in Python within a Linux environment
• Collaboration skills to partner with data scientists and pipeline teams, ensuring ML models are operationalized effectively from concept to production deployment
• Working knowledge of distributed or parallel processing
• Excellent communication skills, capable of explaining technical topics to diverse audiences
• Master’s degree in a STEM or analytics-focused field or equivalent work experience

What Makes You Stand Out: 

• Experience building geospatial or sensor-driven data products from scratch
• Practical knowledge of remote sensing, satellite imagery, or related geospatial domains
• Familiarity in geospatial libraries (e.g., GDAL, rasterio, shapely, geopandas) for remote sensing or geospatial data
• Knowledge of coordinate reference systems, geometry manipulations, and common data formats (GeoTIFF, GeoJSON, etc).
• Expertise in containerized workload orchestration (Docker, Kubernetes) at scale, leveraging massive parallel processing across thousands of concurrent workloads.
• In-depth familiarity with workflow orchestration platforms (e.g. Temporal) for real-time data pipelines, enabling high-throughput ingest and low-latency inference
• Proficiency in model optimization techniques, ensuring minimal latency and efficient resource utilization in production environments

#LI-REMOTE

Application Deadline:

May 21, 2025 by 11:59pm PST

Benefits While Working at Planet:

These offerings are dependent on employment type and geographical location, based upon applicable law or company policy.

• Comprehensive Medical, Dental, and Vision plans
• Health Savings Account (HSA) with a company contribution
• Generous Paid Time Off in addition to holidays and company-wide days off 
• 16 Weeks of Paid Parental Leave
• Remote-friendly work environment 
• Wellness Program and Employee Assistance Program (EAP)
• Home Office Reimbursement
• Monthly Phone and Internet Reimbursement
• Tuition Reimbursement and access to LinkedIn Learning
• Equity
• Commuter Benefits (if local to an office)
• Volunteering Paid Time Off

Compensation:

The US base salary range for this full-time position at the commencement of employment is listed below.  Additionally, this role might be eligible for discretionary short-term and long-term incentives (bonus and equity). The final salary range is determined by job related experience, skills and location.  The range displays our typical hiring range for new hire salaries in US locations only.  Your recruiter can share more about the specific salary range for your preferred location during the hiring process.