Backend Machine Learning Engineer
- Bachelor's or Master's degree in Computer Science, Engineering or a related field.
- 5+ years of experience building backend systems or data pipelines in Python, with a focus on performance, scalability and maintainability.
- Strong experience working with scientific or numerical computing libraries (numpy, pandas) and data engineering patterns (batch, streaming, ETL).
- Strong understanding of software engineering principles (modular design, testability, observability, CI/CD).
- Experience collaborating on system design, architecture decisions and trade-offs.
- Ability to work autonomously within a team, driving initiatives end-to-end while communicating effectively.
- Comfortable navigating ambiguity and proactively clarifying requirements
- Experience with AWS cloud services (S3, Lambda, ECS, Athena, Glue, Step Functions, IAM).
- Experience working with geospatial data (GeoJSON, shapely, geopandas) and distributed datasets.
- Experience designing data models and working with SQL and NoSQL databases (PostgreSQL/PostGIS, DynamoDB, etc).
- Exposure to infrastructure-as-code (Terraform) or containerisation (Docker).
- Experience with Typescript, to be able to read code
- Interest in machine learning operations (MLOps), model integration or data-driven products.
- You are part of a cross-functional, feature-oriented team delivering end-to-end functionality.
- You take ownership of data flows, backend components, and architecture decisions.
- You help evolve our data platform and pipelines — balancing robustness, performance, and cost.
- You contribute to improving our engineering practices — from testing to documentation to developer experience.
- You collaborate closely with the science team to integrate models and ensure seamless data interoperability.
- You participate in technical design discussions, contribute to our technical direction, and help mentor more junior engineers.
- You work in Agile sprints, focusing on delivering business value iteratively.
- You take part in the full lifecycle of development: from problem discovery to deployment, release notes, and demo.
- You have fun along the way. We are doing something good and something cool. We are a friendly bunch. We have many social channels and events. There is no reason we can’t work hard and laugh at the same time!
- Be part of a high-growth startup from the ground up
- Apply AI and data to global nature-based challenges
- Build mastery and push the boundaries of your craft
- Learn from a diverse, cross-functional team
- Access competitive pay, equity, and meaningful benefits
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