Echo Labs - Senior Machine Learning Engineer

Echo Labs is building a scientific and technical foundation for ecological intelligence: a multimodal system to measure, model, and forecast Ecosystem Condition as a dynamic property. We are a collaborative and interdisciplinary team of scientists and engineers engaged in a planetary moonshot – with a public good mission, operating like a start-up. We are a new Focused Research Organization (FRO) supported by Convergent Research and funded by the Advanced Research and Invention Agency to pursue high-risk, high-reward science in the public interest.

About this role

Echo Labs is seeking a Machine Learning Engineer to establish the inner workings of Echo's technical stack. Inspired by lab-in-the-loop biology systems, we want to create a data and modelling infrastructure that enables rapid iteration and learning for ecology. You will build and maintain the data pipelines, cloud infrastructure, and experiment tooling that power Echo's modelling work. You will work alongside a Director of Modelling & Data Infrastructure and the CTO to turn ecological data into model-ready inputs and develop reproducible modelling pipelines as we iterate. We want our technical platform to be excellent, yet built incrementally with only what is needed to make progress. We see immense opportunity to bring technical excellence to ecological data and modelling, and want you to help us define the mechanisms that will bring this vision to life.

Core Responsibilities

• Data Infrastructure: Build data ingestion systems for multimodal ecological data: in situ sensor networks, acoustic files, imagery, video, and laboratory measurements. Implement and monitor QA/QC checks: completeness, format validation, outlier flagging, metadata accuracy. Identify and implement relevant metadata standards. Develop systems that can handle partial, noisy, heterogeneous field data.
• Machine Learning Infrastructure: Design and implement reproducible modeling pipelines with experiment tracking, versioning, and artifact management. Establish infrastructure for rapid model iteration prioritising experiment velocity over model scale. Create pathways from research prototypes to production-grade tools as outputs mature. Maintain cloud infrastructure and tooling.
• Research Tooling: Collaborate across teams to explore how the representation of an ecosystem manifests in structured data, and how model architectures can reflect ecological realities. Work with internal stakeholders to develop tooling to support interpretability and communication of results. Maintain reproducible code, documentation, and example notebooks.

Required Profile:

• 5 years in ML engineering, data engineering, or applied ML research.
• Fluency in Python; experience with PyTorch or equivalent deep learning framework.
• Experience building data pipelines: ETL, data validation, format standardization at non-trivial scale.
• Working knowledge of cloud platforms (AWS or GCP): object storage, compute provisioning, basic networking.
• Comfort with version control, CI/CD, and reproducible experiment workflows.
• Ability to work independently on well-scoped tasks and flag blockers early.

Highly Valued Experience:

• Background in ecology, environmental science, Earth observation, or prior work with ecological datasets.
• Experience with geospatial data: satellite imagery, raster processing, coordinate systems, STAC metadata.
• Experience with audio/acoustic data processing or bioacoustic analysis tools.
• Working knowledge of R.
• Contributions to open-source scientific or ML software.

£125,000 - £145,000 a year

Progression

In the first six months, you'll own specific pipeline components and have data flowing reliably through the system. You'll be running experiments alongside the Director, contributing benchmarking suite components, and building QA/QC tooling that the team relies on daily. By the end of Year 1, you'll have increasing autonomy over infrastructure decisions and a hand in shaping the Year 2 scaling plan as Echo moves from existing datasets to its own national sampling campaign.

Outro

We’re bringing together top talent from academia, industry, and startups to build a new model for innovative R&D. We are committed to creating an inclusive and diverse workplace where everyone has the opportunity to thrive. We believe in hiring individuals based on their unique talents—not on race, color, religion, ethnicity, gender, gender identity, sexual orientation, disability, age, military or veteran status, or any other characteristic protected by law or our company policies. We are more than a proud Equal Employment Opportunity employer. Our goal is to foster a healthy, safe, and respectful environment where all employees are valued and treated with dignity.