Staff Machine Learning Engineer
****PLEASE NOTE WE ARE ONLY SEEKING CANDIDATES IN SPAIN OR COLOMBIA****
As a Staff Machine Learning Engineer you will be responsible for the ML/AI roadmap at CTL, from its inception to execution. You will partner closely with a cross-disciplinary group of staff, including a mix of engineering, product, data and operational teams. Through this work, you’ll help to create operational efficiencies that will increase our organization’s impact and mission of supporting people in crisis.
As a Staff Machine Learning Engineer, you will
- Fully own the model pipeline, from data collection and labeling to deployment.
- Identify product and operational opportunities to build solutions that will create efficiencies.
- Work with Product Managers and Engineering teams to get use cases to production.
- Establish an effective partnership and collaboration with subject matter experts including Clinical Supervisors, Learning Experts and Coaches.
- Communicate and share model performance and impact in a digestible way with the rest of our organization.
- Consider ethics, equity, security, privacy, and confidentiality, in all work.
- 6+ years of experience in the Data Science space.
- Theoretical and practical understanding of ML/AI models both for structured and unstructured data.
- Proficiency in SQL and Python and the relevant ML libraries.
- Ability to write clean and modular code and work with version control tools.
- Ability to create project plans to identify tasks, milestones, and deliverables.
- Experience building, deploying and maintaining data pipelines in Python and Scala (or PySpark).
- Knowledge of NLP techniques and the associated ML/AI models.
- Experience with the Hugging Face ecosystem or other AI libraries.
Salary Range: $126,000 - $178,000
This range is provided by Crisis Text Line. Your actual pay will be based on your skills, experience, location, and applicable law (such as local minimum wage laws). We pay competitively in the tech-forward nonprofit space and offer a robust benefits package.