Sr. Machine Learning Scientist

Found in: Jooble CA O C2 - 1 week ago

Toronto ON, Canada Recursion Full time

The Impact You’ll Make

As a Senior Machine Learning Scientist, you will be contributing to dynamic short-term projects that derisk novel capabilities that significantly contribute to Recursion’s future roadmap. You will contribute to building ML models across a range of data types including vision and other Omics that will power a diverse range of use cases in drug discovery. By leveraging your experience in machine learning, software engineering, infrastructure, and data, you will:

  • Build and curate large scale datasets for training machine learning models across multiple modalities
  • Develop new algorithms and architectures and train large models on these datasets
  • Explore properties of the resulting representation model on a range of benchmarks
  • Develop new benchmarks for interpreting how models are behaving
  • Work collaboratively alongside machine learning engineers, researchers, and product managers
  • Work with other teams to support deploying these models to Recursion’s platform

Location:

This is a hybrid position based at our office in Toronto, Ontario, Canada

The Team You’ll Join

Recursion has built a unique dataset (over 1 million unique biological perturbations profiled in a high-dimensional biological assay, making up nearly 20 PB of highly-relatable data) and a unique approach to phenotypic drug discovery using our inference-based Recursion Map to predict tens of billions of relationships between disease models and therapeutic candidates. Additionally, we’ve generated large-scale Omics datasets to complement our phenomics data.

You will join a team of machine learning engineers, scientists, and data scientists working on prototyping and derisking capabilities that will power a range of use cases at Recursion. The broader team you’ll join includes an interdisciplinary set of scientists, biologists, and product managers that together enable generating new datasets to power the models being developed by this team. The capabilities you bring to the team will help us scale our machine learning models and develop net-new capabilities that don’t currently exist

The Experience You’ll Need

  • Expert knowledge of machine learning theory and practice including the ability to:
  • Curate and build datasets
  • Experience with biological data (microscopy, histology, transcriptomics, clinical data)
  • Develop net-new algorithms and architectures, particularly leveraging self-supervised learning
  • Run quick and focused computational experiments evaluating project feasibility
  • Work closely with biologist to develop new training and benchmarking datasets
  • Interpret and develop benchmarks for assessing model performance
  • Tools: PyTorch, NumPy, Pandas, GCP, Hybrid Cloud, Linux, CUDA, Docker, Kubernetes, BigQuery, large scale distributed systems

How You’ll be Supported

  • You will join an interdisciplinary team that’s having a major impact internally and externally
  • You will contribute to conference publications and have the opportunity to attend major ML conferences
  • You will collaborate with other cutting edge teams at Recursion, including Valence Labs at Mila in Montreal

At Recursion, we believe that every employee should be compensated fairly. Based on the skill and level of experience required for this role, the estimated current annual base range for this role is:

  • Developing: 215,000 CAD
  • Skilled: 226,000 CAD
  • Expert: 242,000 CAD

To learn more about our level within levels, click here .

You will also be eligible for bonuses and equity compensation + our comprehensive benefits package for Canada based candidates. The range displayed on each job posting reflects target ranges for Canadian new hire salaries and is determined by job, level, and market factors.

During the interview selection process, you will connect with a Talent Acquisition Partner who will be your advocate and ally to ensure you receive the appropriate compensation that meets your needs for your skills, experience, and relevant education/training, while also reviewing our very competitive total rewards package.

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