Applied Machine Learning Engineer

ABOUT DEEP SCIENCE VENTURES

Deep Science Ventures (DSV) is a venture creator combining available scientific knowledge and founder-type scientists into high-impact ventures to build a future where both humans and the planet thrive. DSV operates in 4 sectors: Agriculture, Climate, Pharmaceuticals and Computation, tackling the challenges defining those areas by taking a first-principles approach and partnering with leading institutions.

ABOUT THE ROLE

In 2023, DSV is launching Deep Science Ventures College to exponentially increase the number of people solving the world’s hardest problems through our first accredited training program, the Venture Science Doctorate. We will be building on DSV's proven process of identifying and addressing reasons for failures in key science-driven areas from climate change mitigation and cancer to heterogeneous compute. To date, this has been a long and manual process which limits how rapidly we can address these challenges and how fast any science-driven company can move. With the recent advancements in transformers, semantic search and learning analytics, we see a path to massively accelerate and scale this work to give not only our team but all researchers superhuman powers and, in time, to reduce the cost of science to near zero. We have demonstrated the key principles in-house and are looking to build a team to optimise and productise this work and help us scale to take on hundreds of thousands of challenges.

KEY RESPONSIBILITIES

• Design, train and deploy custom transformer-based large language models to accelerate the speed of scientific discovery based on our existing tried and tested venture design approaches.
• Design and evaluate model performance and accuracy metrics relevant to speed and quality of scientific discovery and product development.
• Lead data strategy integration across internal sources (including text, video and audio data) and external sources and products.
• Building systems to continually fine-tune leveraging human experience to continually improve the effectivness of the model.
• Auto-generate into external communication material

Requirements

• Machine learning practitioner who thrives on the question of how to make users superhuman by continually optimising models.
• Has worked in fast moving start-up environments from early 10s of users to hundreds of thousands of users.
• Up to speed with state-of-the-art in transformers and particularly Large Language Models with strong opinions and ideally practical experience in embedding, LLM-based reasoning/classification, vector databases, semantic search, retrieval strategies and making LLM interfaces fast enough for scale.
• Experience with fine-tuning and model optimisations (for performance and inference speed/cost).
• Comfortable leveraging managed services for fast prototyping.
• Ability to learn – you learn new concepts and skills rapidly and look to understand things in an in-depth way
• Confident in designing analytics pipelines to track and optimise user performance and the impact of changes to the model

Good to have

• Experience with deploying human-in-the-loop RL or other ‘instruction-tuned’ approaches.
• Experienced in MLOps for later scaling

Benefits

• Competitive salary;
• Flexible working environment (remote or hybrid);
• Rapid personal development and massive learning curve;
• Working with some of the most ambitious and impact-driven individuals you'll ever find;
• Help build the next generation of hard tech ventures.

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