About the role:

As a Machine Learning Engineer at Bioptimus, you will have the opportunity to improve medical research using state-of-the-art machine learning algorithms. You will work with interdisciplinary teams with both machine learning and biomedical expertise to build foundation models of biology that will unlock AI applications in biomedical innovation.

What you'll be doing:

As an expert Machine Learning Engineer with experience in large-scale deep learning, you will:

Join a team setting the foundations and paving the way for the development and application of foundation models in biomedical research
Drive the implementation and application of advanced machine learning methods, in particular in foundation models, representation learning, large language models and generative AI, to relevant internal and external projects
Contribute to methodological research in key areas to unlock novel applications in collaboration with research scientists.

Depending on your level of experience, you will have the opportunity to supervise a team and lead ambitious projects.

Who we are looking for:

The successful Machine Learning Engineer will have a ‘team-first’ kind of attitude; be independent, curious and detail-attentive; thrive in a dynamic, fast-paced environment; and be fun to work with.

And

Demonstrated practical expertise in representation learning/large language models/generative AI, including prior experience of implementing, training and/or deploying large neural networks
Strong analytical skills
Excellent command of coding in python, and excellent grasp of programming best practices
Passionate about the intersections of healthcare/biology and AI
MSc or PhD in Computer Science-related field (machine learning, applied mathematics, computer science, software engineering) or equivalent experience;
Strong expertise with deep learning frameworks (Tensorflow, PyTorch, Jax, etc.) : you should already master one of these frameworks and implemented large neural networks in an industrial environment or equivalent
Excellent written and oral communication skills
Highly organized and meticulous about details; capable of tackling complex problems logically and effectively

Ways to stand out:

The following are not necessarily required, but would be a plus for the Machine Learning Engineer:

Experience with developing and profiling distributed model training on GPUs, with both data and model parallelism. Ability to modify the model architecture to improve the efficiency of training and inference
Experience in biological data analysis: proteins, bulk or single-cell omics, histopathology, etc
Extensive post-master or good level of post-doctoral experience; or the equivalent experience industry experience
Experience managing multi-stakeholder research projects
Publications in high-impact journals (JMLR, TMLR, ...) and conferences (NeurIPS, ICLR, ICML, …)

Our company embraces remote work. We follow the guidelines below:

You should be able to attend retreats and events in the Paris office (around once a quarter).
Remote applicants are expected to have at least a 5-hour overlap with the team in Paris.
Since we also value in-person work and collaboration, all things being otherwise equal, we will prefer candidates that can connect on a regular basis with the team in Paris.

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*** Machine learning and software engineering skills *** (candidates will be expected to demonstrate and justify their self-assessments during the interview process)

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What amount of experience do you have with parallel training? *

What amount of experience do you have with large ML model optimisation? *

What scale of model have you pre-trained? *

What scale of model have you fine-tuned? *

What training dataset scale have you worked with? *

What is your experience with multi-modal models? *

What is your experience with the evaluation and interpretability of models? *

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*** Biology skills *** *

What is your level of expertise in biology?

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How familiar are you with single-cell omics? *

How familiar are you with histology? *

How familiar are you with genomics? *

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