# Flagship Pioneering ML Internship

**Company**: Flagship Pioneering
**Location**: Cambridge, MA
**Job type**: internship
**Salary**: $7,000 - $9,000 per month
**Category**: Engineering
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/flagshippioneeringinc/jobs/8782467002?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_1c256c02-fc5

## Description

Flagship Pioneering is seeking a Machine Learning Intern to join their team in Cambridge, MA.

The role involves collaborating with Flagship Origination Teams and fellow ML scientists/engineers to explore the possibilities of artificial intelligence.

Responsibilities:

- Collaborate with teams to explore new ideas and build a 'future backwards' case for what needs to be true for them to become a reality

- Explore tradeoffs across multiple options, including what and how much data is needed and where it might come from

- Work towards implementation and evaluation plans that enable addressing real uncertainties

- Implement, evaluate, and iterate on projects

- Leverage Flagship's scalable GPU compute resources, data from their ecosystem, and extensive expertise available across their teams

- Implement benchmarking capabilities needed to measure what matters and evaluate designs

Requirements:

- Computational scientists and engineers with broad interests and experience applying deep technical experience to out-of-distribution scientific questions

- Strong candidates will combine software engineering fundamentals with a readiness to embrace agentic development tools/practices and have experience implementing machine learning models and workflows across multiple model classes or scientific domains

The pay range for this role is $7,000 - $9,000 per month.

## Skills

### Required
- machine learning
- software engineering
- AI

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/flagshippioneeringinc/jobs/8782467002?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)
