At a Glance
- Tasks: Join a diverse team to develop and enhance ML models for climate risk solutions.
- Company: Climate X is a tech company focused on tackling climate change with innovative solutions.
- Benefits: Enjoy flexible hours, training budgets, and a dog-friendly office with plenty of snacks!
- Why this job: Make a real impact on climate resilience while working with top-tier clients and cutting-edge technology.
- Qualifications: Experience in building ML models, strong Python skills, and a collaborative mindset are essential.
- Other info: We value diversity and welcome applicants from all backgrounds.
The predicted salary is between 36000 - 60000 £ per year.
Machine Learning Engineer
About Us
Climate X is a purpose-driven technology company, backed by GV (Google Ventures), Western Technologies (early investors in Meta, Google, Palantir), Commerz Ventures, Pale Blue Dot, Deloitte, and other world-class investors. We’re a wonderfully diverse, growing team with physical offices in London and New York City.
Demand for Climate X is growing fast, and we need to build our team! You’ll be at the front of a nascent industry, working as part of a fantastic and diverse team, doing things that you can be proud of. We’re excited to have the opportunity to speak with you during this process.
Our mission
To deepen the understanding of our changing planet and inspire meaningful action.
What we do
- We’re helping the world become more resilient to the impacts of physical climate risks (including floods, fires, storms and more). In doing so, we help drive positive global impact aligned to many of the UN’s Sustainable Development Goals (SDG’s).
- Our team builds cutting-edge, peer-reviewed science (incorporating climate projections, remote sensing data) and translates that into financial impacts (to the value of assets or business disruption linked to failure of critical infrastructure) that our customers in the financial services industry use to identify, manage and mitigate those risks.
- Climate X’s customers include the world’s largest banks, asset managers and insurance companies including CBRE, Standard Chartered, Virgin Money and Federated Hermes, as well as a partnership ecosystem that includes Deloitte, Capgemini and AWS. Combined, they manage over $6.5 trillion of assets.
- Customer’s choose us thanks to our ecosystem of products that help solve real human problems, and drive tangible business benefits to our customers. They love our customer-centric mindset, as well as our pace of innovation in the market.
The impact you’ll own
As Machine Learning Engineer, you will join an interdisciplinary team of other Data Scientists, Climate Scientists and Geospatial experts, collaborating closely with our Engineering and Product teams to deliver impactful products to our clients.
In this role you will:
- Develop and enhance existing ML codebases, especially around our NLP product.
- Conduct research into new techniques and algorithms to optimise performance and accuracy.
- Fine-tune domain-specific LLM models to meet business requirements.
- Run statistical analyses to assess model performance and extract meaningful insights.
- Build visualisations to communicate findings and facilitate wider understanding across the business and to our clients.
Essential Skills
- Proven experience building and deploying end-to-end ML models (from data preparation to monitoring in production).
- Strong grasp of ML techniques (regression, classification, clustering), and strong experience with Python ML libraries (sklearn, spaCy, NumPy, SciPy etc.).
- Experience using Git for version control and familiarity with CI/CD pipelines.
- Comfortable with data visualisation tools (Matplotlib, Seaborn etc.).
- Experience using cloud platforms (AWS, GCP, Azure) for ML pipelines.
- Strong communication skills – able to explain technical details to non-technical stakeholders.
- A collaborative mindset and eagerness to learn from others in a multi-disciplinary team.
Desirable Skills
- Experience with web scraping using Python (such as BeautifulSoup, Scrapy, Selenium, Requests or others) is a plus.
- Exposure to MLOps frameworks (such as MLFlow, Weights and Biases).
- Knowledge of the financial services or real estate domain from a climate risk perspective, to inform a basic understanding of where data science is being applied, allowing for better context and interpretation of results.
- Experience with processing and analysing geospatial data using Python (geopandas, GDAL, etc.) and/or other GIS software (such as QGIS) is a plus.
Benefits
- Contribute to a business making purposeful impact related to climate change.
- Monthly training & conference budget to help you upskill and develop your career (£1,000 per year).
- 6 monthly appraisals and 12 monthly pay reviews.
- Pension contribution scheme.
- Flexible hours and hybrid working (3 days/week in office; core hours 10am-4pm).
- Mental Health and Wellbeing support via Oliva.
- 25 days holiday, plus Bank Holidays, annual 3-day Christmas-closure, and half day on your birthday (36.5 days total!).
- Optional quarterly socials, dinners, and fun nights out.
- A fully stocked supply of snacks, fruit, and refreshments for the days when you are in the office.
- Cycle to work scheme via gogeta.
- Enhanced maternity and paternity.
- Pawternity.
- Dog friendly office (official residence of Alfie, Chief Mischief Officer).
Equal Opportunities
Climate X are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are committed to creating an inclusive environment for all employees and welcome applications from individuals of all backgrounds. #J-18808-Ljbffr
Machine Learning Engineer employer: Climate X
Contact Detail:
Climate X Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarize yourself with the specific ML techniques mentioned in the job description, such as regression, classification, and clustering. Being able to discuss these techniques confidently during your interview will show that you have the necessary expertise.
✨Tip Number 2
Highlight any experience you have with NLP products, especially if you've worked on enhancing existing ML codebases. Be prepared to share examples of how you've optimized performance and accuracy in your previous projects.
✨Tip Number 3
Since strong communication skills are essential for this role, practice explaining complex technical concepts in simple terms. This will help you demonstrate your ability to communicate effectively with non-technical stakeholders.
✨Tip Number 4
If you have experience with cloud platforms like AWS, GCP, or Azure, make sure to mention it. Being comfortable with these platforms is crucial for building ML pipelines, and showcasing this knowledge can set you apart from other candidates.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Understand the Company Mission: Before applying, take some time to understand Climate X's mission and values. Highlight how your skills and experiences align with their goal of addressing climate risks and driving positive global impact.
Tailor Your CV: Make sure your CV reflects your experience in building and deploying ML models, especially focusing on relevant projects that demonstrate your proficiency with Python ML libraries and cloud platforms. Use specific examples that relate to the job description.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss your collaborative mindset and eagerness to work in a multi-disciplinary team, as well as any relevant experience in the financial services or climate risk domains.
Showcase Communication Skills: Since strong communication skills are essential for this role, consider including examples in your application that demonstrate your ability to explain technical concepts to non-technical stakeholders. This could be through past experiences or specific projects.
How to prepare for a job interview at Climate X
✨Showcase Your ML Experience
Be prepared to discuss your previous experience in building and deploying end-to-end machine learning models. Highlight specific projects where you utilized techniques like regression, classification, or clustering, and be ready to explain the impact of your work.
✨Demonstrate Your Technical Skills
Familiarize yourself with the Python ML libraries mentioned in the job description, such as sklearn, spaCy, NumPy, and SciPy. Be ready to discuss how you've used these tools in past projects, especially in relation to NLP and model optimization.
✨Communicate Clearly
Since strong communication skills are essential, practice explaining complex technical concepts in simple terms. Think about how you would convey your findings to non-technical stakeholders, as this will be crucial in your role.
✨Emphasize Collaboration
Climate X values a collaborative mindset, so be prepared to share examples of how you've worked effectively in interdisciplinary teams. Discuss how you’ve learned from others and contributed to team success in past roles.