Software Engineer

Department: Data Science

Employment Type: Permanent employee

Schedule: Full-time

Seniority: Experienced

Location: London Hybrid - 4 Day Work Week

Climate Policy Radar is a non-profit organisation building open databases and research tools so people can discover and understand complex information, in particular long-text documents, on climate, nature and development. Our data and tools help governments, researchers, international organisations, civil society, and the private sector to understand and advance effective climate policies and deploy climate finance. Harnessing data science and AI, and pioneering the application of natural language processing to this domain, our work renders previously unstructured, siloed data more readable and accessible.

We are a team of ~30 technologists and climate policy experts who care a lot about the ‘how’ (our values and culture) as well as the ‘what’. As part of that, we have embraced a flexible, hybrid approach to work, including a 4 day workweek.

We are looking for a Software Engineer to join our data science team.

This is a growth hire, adding a software engineer to Climate Policy Radar's data science team. Reporting to the Head of Data Science, you'll be a fully integrated member of our team. You're the person who will turn a promising prototype into something real - deploying models, automating pipelines, and building the infrastructure that connects us to other engineering teams.

You'll pair frequently with data scientists to understand their needs, and work across API development, cloud infrastructure, front-end demos, and automation. A lot of the team's best ideas are currently bottlenecked by our engineering capacity; this role exists to unlock them.

About the data science team

Climate Policy Radar's engineering org is split into a few functional teams: programmes (domain experts), platform (high-quality data sharing), application (user-facing tools), and data science (models and evaluation). Despite those splits, most of our work is cross-functional.

The data science team builds the models that power our search engine, classifiers, and LLM workflows, and the evaluation frameworks that keep them honest. Our work directly informs policy decisions, so we care a lot about evaluation, monitoring, and minimising bias. We're research-informed but production-focused, and we default to working in the open by publishing datasets, models, and papers wherever we can. We also share what we've learned through blogs, papers, and public talks.

Tech preferences

The vast majority of the data science team's work is written in Python, so we'll expect you to be a fluent reader/writer!

In addition, here's an (unordered) list of tools which we work with regularly. We're always open to new suggestions of tools you've had success with, but familiarity with these tools will help you integrate with our existing stack:

• ML & NLP: PyTorch, Huggingface Transformers, Pydantic AI, Pandas, Spacy

• APIs & backend: Python, FastAPI, Pydantic

• Data & infrastructure: DuckDB, Snowflake, PostgreSQL, Docker, AWS (ECS, S3, etc.), Pulumi, Prefect, GitHub Actions

• Search: Vespa

• Testing, evaluation & monitoring: Pytest, Hypothesis, Weights & Biases, Posthog

• Development tools: GitHub, Cursor, Claude Code

What will you be working on?

Climate Policy Radar's core product is a search engine for 30,000+ climate policy documents, powered by ML models and classifiers developed by our data science team. The team has three major strands of work planned for 2026:

1. Working on our search engine to improving information retrieval

2. Working on classifiers to create more connections between our core data

3. Building workflows with LLMs which are able to synthesise disconnected pieces of evidence into a coherent answer

Your engineering expertise will unlock progress across all three strands. Over the next year, you're likely to:

• Work closely with the other engineering teams to help shape our research & development priorities

• Deploy LLM agent workflows that data scientists have developed, taking them from notebook prototypes to production services

• Build and deploy front-end demos (using Streamlit or similar) that illustrate new features and help stakeholders understand what's possible

• Pair with data scientists to build and deploy APIs for new data sources and model capabilities, making them accessible for other teams to prototype around

• Automate the running of evaluation scripts, enabling the team to iterate quickly and confidently on model improvements

• Develop data pipelines to populate evaluation sets for new classifier training and testing

• Build and maintain infrastructure for running ML models in production, with consideration for performance, cost, and reliability

• Implement monitoring, logging, and alerting for ML systems to catch issues before they impact users

• Champion engineering best practices within the data science team, helping to improve code quality, maintainability, and reliability across our work

To hit the ground running in this role, we anticipate that you'll have the following:

• 3-5 years of software engineering experience, with a track record of building and deploying production services

• Excellent Python programming skills - the vast majority of the data science team's work is written in Python, and you'll be fluent in reading and writing it

• Some familiarity with TypeScript/JavaScript (ideally modern front-end frameworks like Next.js)

• Strong foundations in software engineering best practices: requirements gathering, code review, testing, version control, documentation

• Solid experience with AWS services (ECS, Lambda, S3, RDS, etc) and cloud infrastructure in general

• Experience building and deploying APIs (REST or similar) that are consumed by other teams or services

• Comfortable working with Docker and containerized deployments

• Familiarity with CI/CD pipelines and automated testing

• Reasonable fluency with data science terminology and tools - you don't need to be a data scientist, but you should be comfortable talking about model performance, evaluation metrics, training pipelines, and experimental results

• Deeply collaborative nature - this role involves constant pairing with researchers, asking questions to understand needs, and translating loose research ideas into solid implementations

• Pragmatic approach to infrastructure - knowing when to use managed services vs custom solutions, and how to balance speed with reliability

Nice to haves

In addition to the above, we'd love to hear from you if you've got experience with any/all of these:

• MLOps or experience deploying machine learning models to production

• Working with LLMs and agent frameworks (we like Pydantic AI, but we're open to others)

• Infrastructure-as-code tools like Pulumi, Terraform, or CloudFormation

• Workflow orchestration tools like Prefect, Airflow, or similar Working with search engines or vector databases for information retrieval (Vespa, Elasticsearch, etc)

• Building data pipelines or ETL systems

• Developing around the Wikidata / Wikibase / Wikimedia ecosystem

• Working with knowledge graphs, or graph databases (Neo4j, etc)

• Experience working in research-focused or cross-disciplinary teams

We are looking for candidates with significant experience in highly collaborative cross-functional teams, excitement about working in a startup/scaleup environment and all that brings. 

We are a mission driven organisation, and work best with people who have strong alignment with our values. We care about them deeply.

We actively encourage applicants from diverse and historically underrepresented backgrounds. Not sure if you tick all the boxes but feel like you align with our values, are excited about working in Climate Change and AI and have the potential to do well in the role? Click apply! We’d love to hear from you.

• Salary ranging from £65k - 75k pa, depending on experience

• A deep commitment to employee wellbeing, including policies such as 4 day workweek (same pay, Fridays off), unlimited annual leave, and a wellbeing allowance

• A vibrant, collaborative, empathetic work culture that thrives on innovation and the impact of our work

• Hybrid work environment (2 days a week) in London, currently at Sustainable Ventures in County Hall with a plan to move to TechSpace in Goswell Road in late March. We welcome conversations about flexibility and remote work to accommodate individual needs

We know that applying for a new job can be full of uncertainties - and we aim to reduce those by communicating clearly. Our process is made of several stages (see below). After each stage, we’ll contact you as soon as we can and no longer than 2 working days, to let you know if you will be progressing to the next stage.

If you have a disability or special need that requires accommodation during the process of application and selection, please let us know.

Process:

1. 30-60 minute screening call with our recruiter

2. 60 minute remote behavioural interview with two team members, focusing on communication and culture fit

3. Task:

   1. 2-3 hour take-home task (scheduled to be sent and returned at a time that suits you)

   2. 60 minute interview to discuss your task

4. 30 minute in-person final fit interview with CEO and Head of People (you’ll also have the opportunity to meet the team at this stage - not a part of the interview, but gives you the opportunity to get to know the team and learn more about us in an informal setting)

5. Offer subject to reference

We’ve all felt the anxiety of waiting to hear back from interviews, so we will contact you no later than 2 working days after each interview to let you know if you will be progressing.

At Climate Policy Radar, We are committed to fostering a workplace that is inclusive and equitable. Climate Policy Radar welcomes applicants from all backgrounds and does not tolerate discrimination in any aspect of employment. We actively work to ensure equal opportunities for all, regardless of heritage, ancestry, national origin, citizenship, religion, sex, sexual orientation, gender identity, age, disability, relationship choices, or criminal history, in line with legal requirements. We also consider qualified applicants regardless of criminal histories, in line with legal requirements.

Not sure if you tick all the boxes but feel like you align with our values, are excited about working in Climate Change and AI and have the potential to do well in the role? Click apply! We’d love to hear from you.

If you have a disability or special need that requires accommodation in the process of application and selection, please let us know.