Company Description

Reversing biodiversity loss starts with understanding it. Polliknow transforms conservation efforts by monitoring insect pollinator diversity and abundance alongside other environmental indicators. Our work focuses on providing insights that drive effective conservation strategies. Visit our website or contact us at [email protected] to learn more.

The Role

We're looking for a software engineer with strong fundamentals in classical machine learning and experience (or keen interest) in generative AI and multimodal large language models.

You should be resourceful, determined, and excited about using advanced technology to solve real problems in a startup environment.

You'll be joining as one of our earliest technical hires, working directly with our founder-led team to shape the future of biodiversity monitoring. This is an opportunity to work with real-world data from nature restoration sites across and build ML models that support biodiversity conservation.

What You'll Own

• Build and deploy production ML systems from initial research through to live deployment, managing the complete lifecycle of computer vision models in the field
• Design scalable data infrastructure to handle large ecological datasets, implementing pipelines for ingestion, processing and model serving across cloud and edge environments
• Lead hardware integration for field monitoring devices, adapting models for deployment on single-board computers whilst maintaining accuracy under real-world constraints
• Drive continuous improvement of model performance through monitoring, experimentation and staying current with computer vision and ecology AI research
• Develop robust APIs that connect our ML systems to client applications, ensuring reliable access to species identification and biodiversity metrics

Perfect for you if

• 3+ years of building and shipping software as a Software Engineer, Machine Learning Engineer or similar engineering or technical role
• Computer vision in production where you've deployed CV models that real users depend on, whether from industry production deployments, research implementations or substantial side projects that demonstrate your capability
• Cloud infrastructure experience building data pipelines, managing storage and databases, and orchestrating ML workflows on AWS, Azure or GCP
• Production ML deployment experience including containerising models, managing inference services and troubleshooting systems under load
• Experience working with large datasets and the practical engineering challenges of storage, processing and pipeline optimisation at scale
• Comfortable with early-stage uncertainty where priorities evolve quickly, roles aren't rigidly defined and you'll solve problems outside your typical scope
• You have a bias for action you thrive in ambiguity with an ability to move quickly while maintaining high standards
• Engaged with ML research keeping up with relevant papers and new techniques, testing novel methodologies in both classical and generative AI

Nice to have

• Experience with hardware integration whether professionally or through side projects involving single-board computers like Arduino, Raspberry Pi or ESP32
• Active side projects in ML, hardware, conservation tech or anything that shows you build things for the love of it
• Clear technical communication able to explain complex concepts to both engineers and non-technical stakeholders like ecologists or land managers
• Track record of learning quickly, comfortable with picking up new technologies or frameworks as the work requires

What we offer

Salary and equity incentive scheme, generous annual leave and significant technical ownership. Work directly with founders building systems that support nature restoration, with real influence over both product and company direction.

Interested?

Join us in building technology making nature recovery measurable and achievable.

Send your CV and a brief note to [email protected] with subject line "Software Engineer”

In your note, tell us:

- One specific project where you deployed ML models

- Any links to projects, GitHub/GitLab

- A time you had to quickly learn new technology to solve a problem

*Note: Visa sponsorship is not provided for this role.*

We'll respond within 2 weeks to qualified candidates.