About the job
Position Overview:
As the Senior / Lead ML(Ops) Software Engineer, reporting to the Head of Engineering, you will be part of the core team defining, building, testing, and delivering our GeoAI platform to enable internal and external Data Scientists (DS) and Machine Learning Engineers (MLE) to implement Geospatial AI models. The Senior / Lead ML(Ops) Software Engineer position sits in our Software Engineering team; you will interface with peer software engineers, domain experts, data scientists, machine learning engineers, product designers, and product marketers. You will work at the intersection of AI research activities and software engineering to advance our product roadmap.
As the Lead ML(Ops) Software Engineer, you will:
- Own the design, development, deployment and support of our cloud-scale training services for GeoSpatial Machine Learning models
- Be the Subject Matter Expert ensuring that the Software Engineering team builds and delivers solutions that “just work” for how DS and MLE approach GeoSpatial problems in the real world
- Engage internal collaborators and our end-use partners and customers to understand needs, plan solutions and lead development of deliverables
- Close the feedback loop with Solutions, Engineering and Innovation teams to deliver customer experience and market insights
- Architect and develop serverless solutions that marry products within and across cloud platforms (e.g. GCP, AWS, Azure) that address customers’ needs
- Lead client-oriented API design and development
- Work with designers, product managers, and your fellow engineers to build APIs to support key functionality and use cases
- Actively participate to improve engineering standards, tooling, and processes
- Leverage your experience in early stage startups to make informed decisions that balance speed vs quality
- Focus on designing architectures to solve core problems, not just because it is the latest trend
What you'll bring:
- Three or more years of recent work experience as a practicing Data Scientist or Machine Learning Engineer using PyTorch, sklearn and lightgbm
- Two or more years of recent work experience as a practicing Backend Software Engineer using Python, GDAL and other GeoSpatial tools deployed in the cloud as serverless applications (e.g. GCP Cloud Run) or orchestrated workflows (e.g. Airflow, Argo)
- At least three or more years of recent work experience building around Earth Observation use-cases
- Technical ownership of key areas of a Product, such as one or two critical services
- Demonstrable experience with Python, Pandas, PyTorch, GDAL
- Experience with building distributed computing infrastructure flexible for both on-demand and batch processing
- Self-starter attitude, comfortable working independently and in an entrepreneurial, start-up environment; thrives in high-trust environments with minimal resources/backing
- Exceptional interpersonal communication, both written and oral
- Independence and ownership of work in an entrepreneurial, start-up environment
- Intellectual curiosity and and desire to contribute across cultures in a dynamic global environment
Experiences that are big pluses:
- Applying Geospatial libraries and datasets at scale for user-facing applications
- Developing enterprise PaaS and SaaS products
- Working in early-stage startup environments
- Developing on Google Cloud Platform
- Comfort working in a distributed team environment
Please provide a link to your GitHub repo in your CV.
We are ideally looking for a candidate in the SF Bay Area, CA or New York, NY (fully remote with meet-ups occasionally in-person). We offer a casual and inclusive working environment, competitive compensation, and great benefits. We are a values-driven company, and take great pride in building products that are designed to improve lives and foster development across emerging markets.
Salary range for candidates depending upon level of experience and location: $170,000 - $200,000.
This role requires eligibility to work in the USA for a US company, and infrequent travel may be required.
This version omits specific company names and retains the essence of the original job description
