Senior Machine Learning Engineer

Position:

As the Senior Machine Learning (ML) Engineer, you will apply expertise in Natural Language Processing (NLP) and Large Language Models (LLMs) to develop and implement innovative ML solutions that have the potential to make a significant impact in the lives of children and adolescents struggling with mental health issues. You will work with researchers, clinicians, and software engineers within DAIR and across departments to design, prototype, deploy and maintain applied ML solutions to enhance our digital mental health tools.

This position is based in a professional office environment. It requires the ability to sit or stand for extended periods, operate standard office equipment (including computers, phones, copiers, and printers), perform repetitive tasks, and communicate effectively with others. Occasional lifting of up to 10 pounds may also be required. This role may require occasional travel for meetings.

Responsibilities:

• Design, implement, and maintain machine learning models with a focus on NLP and LLMs for real-world application in the mental health field.
• Perform analyses on large datasets using high-performance computing infrastructures.
• Collaborate with cross-functional teams to integrate ML solutions into research projects and mental health tools.
• Develop and maintain data pipelines for training, evaluating, and deploying models.
• Leverage expertise to create innovative solutions to complex problems using state-of-the-art ML methods.
• Ensure data quality, security, and compliance with privacy regulations.
• Perform additional job-related duties as assigned.

Qualifications:

• Master's degree in Neuroscience, Psychology, Engineering, Computer Science or equivalent combination of education and experience.
• 8+ years of experience in data analysis and data science fundamentals (e.g., algorithms, data structures, data visualization, machine learning).
• 8+ years of experience in at least one scientific programming language (e.g., Python/R, Matlab) and related toolboxes or frameworks (e.g., Tidyverse, Scipy, Sklearn, Polars, Pytorch) is required.
• 5+ years of experience working in a Linux environment, using version control systems (e.g., GitHub), and software virtualization platforms (e.g., Docker).
• 3+ years of practical experience developing and designing production-oriented machine learning technologies and systems, including hands-on experience in NLP and modern ML/NLP.toolchains (e.g., pytorch, tensorflow, SpaCy, HuggingFace, NLTK).
• Experience can be concurrent with education.
• Work cooperatively and contribute to group efforts in a very collaborative, open-source, and multidisciplinary environment.
• Good knowledge of standard office software applications (e.g., Microsoft Word, Excel, PowerPoint, etc.).
• Demonstrated track record of strong technical writing and communication skills.
• Understanding data privacy regulations (HIPAA, GDPR) and research ethics.
• Strong interpersonal skills, as well as strong written and oral communication.
• Ability to independently and responsibly contribute to data analysis and machine learning technologies in clinical contexts.
• Capable of mentoring staff tasked with machine learning model development.
• Identify and address challenges with innovative solutions.
• Skilled in planning, executing, and monitoring projects.

Please upload your CV during the application process.

The Child Mind Institute is an equal opportunity employer and does not discriminate in employment based on race, religion (including religious dress and grooming practices), color, sex/gender (including pregnancy, childbirth, breastfeeding or related medical conditions), sex stereotype, gender identity/gender expression/transgender (including whether or not you are transitioning or have transitioned) and sexual orientation; national origin (including language use restrictions and possession of a driver's license issued to persons unable to prove their presence in the United States is authorized under federal law [Vehicle Code section 12801.9]); ancestry, physical or mental disability, medical condition, genetic information/characteristics, marital status/registered domestic partner status, age (40 and over), sexual orientation, military or veteran status, or any other basis protected by federal, state or local law or ordinance or regulation.