Job details

Machine Learning Cloud Infrastructure Tech Lead

About Deepcell

Interested in helping to develop cutting edge technologies to empower cell biology and save lives? If so, we'd love to talk to you!

Our company is focused on developing a new technology to improve biological research and, ultimately, health outcomes, across all of biology, enabling previously impossible applications. We deliver detailed breakdowns on a cell-by-cell basis, by using deep learning to classify cells based on their morphology. We've launched an internal product and are working to develop a commercially releasable version. Come join Deepcell and make a difference! We're a rapidly growing team of stellar innovators in biomedical engineering, artificial intelligence, and molecular biology. Our technology has won multiple prestigious awards and is backed by top-tier venture capitalists in Silicon Valley including Andreessen Horowitz and Bow Capital.

Responsibilities

As an ML Cloud Infrastructure Technical Lead you will provide technical leadership for a growing team of cloud and machine learning engineers. The team is tasked with leading development of our data architecture, model training, deployment, and evaluation infrastructure. You will also take a leading role in development of model training services that provide optimized models and can be run by non-data scientists for use by both external and internal customers. You’ll build, maintain and improve data processing pipelines that process billions of data points and petabytes of data.

Additionally and specifically, guide the team in providing infrastructure services, starting with computational services: generate class predictions and embeddings by applying models to run IDs, morphometrics by applying morphometric models to run IDs, projections like UMAP, tSNE, PCA from embeddings, and extract clusters. Also help supply data services: cell data and metadata, obtain metadata about runs and datasets, model service, save and retrieve models and model metadata, image service to retrieve images from run IDs, label service to save and retrieve cell attributes (labels) by cell ID.

You’ll work with multiple interdisciplinary teams composed of data scientists, bioinformaticians, biologists and software engineers to help solve hard problems which improve biological research and, ultimately, health outcomes, across all of biology.

Minimum Qualifications

This is an on-site position preferred but willing to consider exceptional remote candidates. Work from home as appropriate.
Bachelors in computer science or equivalent
7 years' software development experience
5 years' experience leading data engineering teams of 5-10 engineers
5+ years' experience running medium to large/complex projects with multiple internal / external dependencies
Familiarity with at least one deep learning framework, such as PyTorch, TensorFlow, or Caffe
Proficient in Python
Has built and deployed scalable cloud data architectures in a commercial setting
Experienced in working with ETL pipelines
Experienced in cloud providers such as GCP and AWS
Strong analytical and problem solving skills
Ability to understand and execute on the company’s mission and values
Maintain a high degree of ethical standard and trustworthiness
Strong technical written and oral communication skills

Preferred Qualifications

10+ years' software development experience
7+ years' experience in leading engineering teams
A track record for delivering Machine Learning projects for a product
Excellent written and verbal communication skills
Strong and proactive communication, natural curiosity. Ambition to apply skills to a wide variety of fields
Familiarity with latest data engineering ecosystems and ideally has contacts within the industry
Be able and open to pick up new skills, work with 3rd party technologies and devices
Experience with MLOps