What if…you could join an organization that creates, resources, and builds life sciences companies that invent breakthrough technologies in order to transform health care and sustainability?
Montai Health is a privately held, early-stage biotechnology company developing a platform for understanding and leveraging complex molecular interactions within organisms to solve global challenges in human health and sustainability. The company leverages a multidisciplinary approach that integrates tools ranging from machine learning and big data to multi-omics and high-throughput screening.
Montai Health was founded in Flagship’s venture creation engine, where companies such as Moderna Therapeutics (NASDAQ: MRNA), Rubius Therapeutics (NASDAQ: RUBY), and Editas Medicines (NASDAQ: EDIT) were conceived and created. Since Flagship’s founding in 2000, the firm has originated and fostered the development of more than 100 scientific ventures, resulting in over $34 billion in aggregate value, 500+ issued patents, and more than 50 clinical trials for novel therapeutic agents.
We are looking for an experienced engineer to join a small and growing team of machine learning and computational biology scientists. This person will work directly with scientists to:
- Improve our computational workflows with an eye towards solid software engineering principles
- Help manage and scale our compute and data resources
- Build and adapt software that scales our ability to develop, run and reproduce intensive computational experiments
We hope to find someone who is an expert in their field and curious to work with experts in molecular/computational biology and machine learning. We are a diverse and multidisciplinary team and love to learn!
- 6 or more years, or BS/MS + 3 years combined experience in software, data & machine learning engineering
- Dependency and environment tracking and management
- Data organization, versioning and provenance tracking
- Code and repository organization
- Applying principles of good software design to data processing and machine learning pipelines (reproducibility, functional decomposition, abstraction design)
- Building, maintaining, documenting and evangelizing software tools for use within a team
- Working directly and communicating effectively with computational scientists (who may have less software engineering experience)
- Communicating directly with business development to stay current on quickly evolving goals
- Machine learning computational workloads (Large data payloads, batching and chunking. Memory, CPU and GPU resource management.)
- Distributed computation methods and cluster management
Specific familiarity with:
- Python 3, Numeric python ecosystem (Some of: numpy, pandas, pytorch, tensorflow, jax)
- AWS (Some of: EC2, S3, Cloudwatch, ECS, EMR, RDS)
- Python dependency management tools (Some of: conda, pip, setuptools)
- SQL and non-SQL database systems (Some of: Postgres, Mongodb, Elasticsearch)
- Cluster management and containerization (Some of: Docker, Kubernetes, AWS EMR, AWS ECS, Spark, Dask, Ray, Distributed PyTorch/Tensorflow)
- Data warehouse/lake frameworks (Some of: Snowflake, Redshift)
- ML-focused data and workflow management frameworks (Some of: DVC, MetaFlow)
- Linux commandline, bash