Advanced Machine Learning Engineer

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

The Machine Learning Engineering team is an interdisciplinary group focused on applying engineering rigour to machine learning problems on the BenevolentAI software platform. To carry out our role, you understand discoveries in machine learning algorithms, as well as the impact of a well-engineered model training and deployment infrastructure with a reliable data pipeline - and innovate on all.

BenevolentAI is committed to growing diverse and inclusive teams. We welcome you to apply if the job description as a whole appeals, even if there are specific areas where you don’t have previous experience!

Primary Responsibilities

• Design and implement reusable and maintainable AI models that solve drug discovery problems.
• Bridge AI research and software development teams to speed up concept-to-production transition.
• Promote machine learning best practices, such as: scalable training and deployment, model introspection and evaluation, new state-of-the-art implementations, etc.
• Promote engineering best practices, such as: code reviews, test-driven development, pair programming, etc.

Additional Responsibilities:

• As a member of the Engineering team, you will be responsible for helping to define new directions and scope for existing and new software.
• As a member of the Target Identification product team, you will be interfacing with users including Drug Discoverers and Bioinformaticians.

We are looking for someone with

• Degree in Computer Science, Machine Learning, Artificial Intelligence (BSc/MSc/PhD) or related academic discipline.
• Strong in at least one programming language (Python preferred).
• Basic algorithms and data structures for software development.
• Knowledge of modern tools for ML such as Tensorflow, Pytorch, etc.
• Knowledge of modern MLOps tools such as Kubeflow, MLFlow, Sagemaker, etc.
• Experience implementing machine learning workflows.
• Awareness and passion for the state-of-the-art in machine learning.
• Understanding of distributed systems and microservice architectures.
• Experience developing at least one of the following software systems: databases, APIs, microservice architectures, web frameworks, core machine learning libraries.
• Experience in at least one of the following software domains: frontend development, backend development, data processing pipelines.
• Basic knowledge of biology, and/or experience working with biomedical data is a plus.
• Knowledge of the pharmaceutical industry, ideally with a focus on drug discovery, is a plus.