Machine Learning Engineer

Zelp; London; On-site; GBP 50,000 - 80,000; Full time

Job description

About ZELP

ZELP is an agricultural technology company committed to creating a sustainable future for agriculture - one that meets the needs of present and future generations, promoting human and environmental health, animal welfare, and social and economic equity.

Our first focus is to develop and scale technology that measures and mitigates methane emissions in the livestock industry. Today, 1.3 billion people rely on the industry for their livelihoods and food security. However, it is the single biggest human-driven source of methane emissions globally, emitting more than both the oil & gas industry and the coal industry.

The deployment of our technologies on a global scale has the potential to drive half the global methane reduction needed by 2030, and to greatly transform the beef and dairy industries.

ZELP was an inaugural winner of the Terra Carta Design Lab, and has received funding and support from the European Commission, Innovate UK, The Global Methane Hub and the Bill & Melinda Gates Foundation.

About the role

ZELP is seeking a highly motivated and skilled Machine Learning Engineer to join our growing team. The ideal candidate will possess a strong engineering foundation with expertise in designing, building, and deploying machine learning models and scalable data pipelines. This role requires a strong understanding of data analysis, machine learning, and software engineering principles, coupled with excellent communication and collaboration skills. The Machine Learning Engineer will be crucial in developing and implementing machine learning solutions that address critical business challenges and contribute to our innovative technology.

Key Responsibilities

1. Collaborate with stakeholders to understand business needs and translate them into scalable machine learning solutions.
2. Design, implement, and maintain robust and efficient data pipelines for collecting, cleaning, transforming, and preparing large datasets.
3. Perform exploratory data analysis (EDA) to understand data characteristics and inform feature engineering.
4. Develop, implement, and deploy advanced statistical and machine learning models to solve business problems, including forecasting, segmentation, optimization, and predictive modelling, with a focus on scalability and production readiness.
5. Conduct rigorous evaluation of model performance and ensure the reliability and stability of deployed models.
6. Collaborate with software engineering teams to integrate machine learning models into production systems.
7. Develop and maintain interactive dashboards and visualizations.
8. Stay abreast of emerging technologies and industry best practices in machine learning engineering and deployment, continuously exploring opportunities for innovation and improvement.
9. Contribute to the development of a data-driven culture within the organization, emphasizing the practical application and impact of machine learning.

Required Skills and Experience:

1. 5+ years of experience in machine learning engineering.
2. Proven experience working with large datasets and deploying machine learning models into production.
3. Expertise in programming languages such as Python, with a strong understanding of object-oriented programming concepts and software engineering best practices.
4. Strong experience in designing and implementing ETL pipelines, data warehousing, and data modelling with a focus on machine learning requirements.
5. Extensive experience with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and model deployment frameworks.
6. Proven experience with solving Sequential modelling problems.
7. Solid understanding of statistical modelling, machine learning algorithms, and data mining techniques with a focus on their practical application.
8. Familiarity with data visualization and reporting tools such as Tableau, Power BI, or Looker.
9. Strong experience with cloud platforms (e.g., AWS, Azure, GCP) for building and deploying machine learning infrastructure.
10. Strong analytical and problem-solving skills, with the ability to translate complex business problems into effective machine learning solutions and production-ready systems.
11. Excellent communication and presentation skills, with the ability to effectively communicate complex technical concepts to both technical and non-technical audiences.
12. Collaborative mindset and ability to work effectively within cross-functional teams, including data scientists and software engineers.

Company Benefits

1. Flexible hours
2. Pension scheme

Interview Process

1. Take-home exercise and technical interview
2. Call with Hiring Manager
3. Culture add
4. Offer