Machine Learning Engineer

Kemioconsulting; London; Hybrid; GBP 50,000 - 90,000; Full time

Job description

We are looking for candidates motivated by advancing drug discovery by translating the best AI and ML research into drug discovery impact.

Position: Machine Learning Engineer
Location: London - Hybrid

A well-funded VC backed start up is on a mission to transform drug design through cutting-edge AI, making the process faster, smarter, and more efficient. They are building an advanced AI ecosystem to solve real-world challenges in drug discovery, partnering with industry leaders to bring impactful solutions to life.

You'll be part of an innovative, collaborative team dedicated to revolutionising the pharmaceutical industry and improving patient outcomes worldwide. If you're passionate about technology, science, and making a global impact, and want to be at the forefront of the future of drug discovery, then this is definitely the opportunity for you.

To be successful in this role, you will need to have an extensive background in Machine Learning engineering within the healthcare or scientific domain.

Key Responsibilities:

1. Integration of machine learning models into impactful scientific products
2. Analyse large-scale biological and chemical datasets to identify patterns and generate actionable insights
3. Contribute to the building of a scalable ML platform that can integrate machine learning models into the drug discovery process
4. Design and build machine learning focused infrastructure for high-throughput data processing
5. Evaluation and improvements to model performance

Experience Needed:

1. PhD in Computer Science, Bioinformatics, Computational Biology or related field.
2. Extensive experience with common machine learning frameworks e.g., TensorFlow, PyTorch, scikit-learn.
3. Must have experience working in scientific domain
4. Python proficiency
5. Experience with big data technologies e.g., Spark, Kafka, and Iceberg.
6. Knowledge of data modelling, database design, and data warehousing concepts.
7. Proficiency in data visualisation tools like Superset, Grafana, or Metabase.

If you are a self-motivated, experienced ML engineer, who is passionate about developing tools for drug discovery, this could be a great opportunity to build something with a great positive impact. If you are interested in finding out if you could be a good fit, please apply to be considered.