We are looking for a highly skilled machine learning researcher with significant experience in generative modeling. You will join an interdisciplinary team of machine learners, protein engineers and biologists, jointly working to change the way that we control biology and cure diseases. In your role you will architect novel generative models with the goal of designing new proteins that are functional in wet lab assays.
Who you are
You are a strong ML researcher with experience in generative modeling. You have led or worked on notable machine learning projects, as documented by your contributions to widely used open source libraries, significant product launches or high impact publications, e.g. at NeurIPS, ICML, ICLR or Nature venues. You have a proven track record of deep expertise in generative modeling.
You are a skillful ML developer. You write ML code that is robust, tested and easy to maintain. You have experience using version control and code review systems. You are a fast prototyper and hacker who can also write beautiful production code. You have experience running training and inference on cloud hardware, parallelizing data and models across accelerators.
You are a data engineer. You have experience building ML data pipelines for the training and evaluation of deep learning models. You are able to analyze the raw data, construct appropriate dataset splits and build pipelines that perform and scale.
You are passionate about model performance. You have an intricate understanding of how ML libraries interplay with hardware and data and love to optimize deep learning models for training / inference speed. You have a deep knowledge of best principles and tricks in architecting deep learning models and use it to optimize how they perform on validation metrics.
You are mission driven and curious. You are passionate about making a positive impact on the world, whether it's for patients, customers or beyond. You are motivated by the end goal and are flexible in adapting to different approaches and methodologies. You are curious about problems, however small or big they appear.
You thrive in a dynamic environment. You work well in a fast-paced setting where goals must be achieved efficiently and urgently.
What sets you apart
You have experience in computational biology or protein design. You have worked on ML-driven projects in biology.
You have a natural science background. You are academically trained in physics, biology, chemistry or other related fields.
Your Responsibilities
Build machine learning models that work in the physical world [~90% of your time]:
Contribute to a careful curation of our training and evaluation data.
Propose and build ML evaluation metrics that align with real world success and company goals.
Quickly prototype generative models against our lead metrics and perform deep analyses of improvements.
Collaborate in a joint codebase with other research scientists, engineers and protein designers, maintaining highest code standards.
Contribute to the maintenance of our compute and ML development infrastructure.
In collaboration with the bio team, plan wet lab testing campaigns and carry out model inference against biological targets to enable their testing in the wet lab.
Quickly learn from wet lab results and feedback data to our models.
Self development [~10% of your time]:
Stay on top of the latest developments in ML.
Gain a strong working understanding of protein and cell biology.
Participate in knowledge sharing, e.g. organize and present at our internal reading group.
Attend and present at conferences.
Apply
We offer strongly competitive compensation and benefits packages, including:
Private health insurance
Pension/401(K) contributions
Generous leave policies (including gender neutral parental leave)
Hybrid working
Travel opportunities and more
We also offer a stimulating work environment, and the opportunity to shape the future of synthetic biology through the application of breakthrough generative models.
We welcome applicants from all backgrounds and we are committed to building a team that represents a variety of backgrounds, perspectives, and skills.
