Machine Learning Engineer

Description

The NYU McSilver Institute for Poverty Policy and Research is committed to creating new knowledge about the root causes of poverty, developing evidence-based interventions to address its consequences, and rapidly translating research findings into action through policy and best practices. 

We are seeking to recruit a machine learning engineer to join our AI Hub. The AI Hub at McSilver has been established to investigate how artificial intelligence-driven systems can be used to equitably address poverty and challenges relating to race and public health, and to provide thought leadership on the implications. At the AI Hub, we are developing a modern ML stack to enable more efficient, equitable, and accessible data analysis for public health research and policy. We have a collaborative team of developers, data scientists, and public health researchers dedicated to leveraging AI/ML for positive impact through public health solutions that better serve underrepresented and marginalized groups. 

Reporting to the Assistant Director for Research, the Machine Learning Engineer will join our ongoing efforts to build public interest technology. As a member of our software development team collaborating directly with our research team, you will apply software engineering and machine learning to large public health datasets including observational, longitudinal, survey, and text data. Your goal will be to build high-performing, secure, robust, and responsible ML systems to help make data analysis tools for our suicide prevention and other public health initiatives. The ideal candidate will have a strong AI/ML background and a track record of building production ML solutions or tooling that have delivered business value. 

Key Skills:

• Deep knowledge of implementing ML solutions using cloud technologies, particularly AWS (e.g., SageMaker, Bedrock, ECS, S3, etc.)

• Proficiency in ML frameworks and libraries (e.g., SciKit Learn, PyTorch, Tensorflow, XGBoost, MLFlow)

• Experience with state-of-the-art ML Fairness techniques and Responsible AI principles

• Strong understanding of data structures, algorithms, and software design principles.

• Ability to communicate results to internal stakeholders as well as the broader ML community via publications in top-tier conferences, industry conference presentations, blogs, and events

• Ability to work in a small, agile team with quick decision-making.

Key Responsibilities:

• Design, develop, and implement machine learning algorithms and systems

• Utilize AWS services and products for ML model deployment and scaling

• Lead projects focused on identifying, avoiding, and mitigating bias in a diverse range of ML applications, including Generative AI.

• Develop and maintain LLM inference pipelines (using fine-tuned and pre-trained models)

• Create and maintain containerized applications using Docker

• Develop robust APIs to integrate ML solutions into existing systems

• Run ML systems experiments and tests

• Deploy ML models to production and with comprehensive model risk management

• Develop AI and ML pipelines for continuous operation, feedback, and monitoring

• Collaborate with data scientists and project/product managers to establish objectives and translate business requirements into technical specifications

• Perform Integration Validation and Verification of developed algorithms

• Stay current with advancements in AI and ML technologies

Qualifications

Required Qualifications:

• Master’s degree or equivalent experience in Artificial Intelligence, Machine Learning, Computer Science, Data Science, or Mathematics

• 3 years of professional experience in Machine Learning, and/or Engineering providing strong ML support

• Strong programming skills in Python 

• Proficiency in developing and deploying ML models on cloud infrastructure

• Experience with data modeling and big data technologies: SQL, NoSQL, Apache Spark, PySpark, Hadoop

• Experience in developing APIs for ML model deployment

• Understanding of software engineering best practices, version control, code review processes, and containerization 

• Knowledge of MLOps and ML security tools, techniques, and platforms, including CI/CD and model monitoring

• Familiarity working in Agile/Scrum environments, utilizing project management tools like Jira and Confluence

Preferred Qualifications:

• Industry certification from AWS (Machine Learning), Google (Professional Machine Learning Engineer), or academic institutions

• Experience with solution architecture and design for scalable ML solutions

• Command of cutting edge data visualization techniques for AI/ML workflows

• Knowledge of and ability to learn statistical and research methods to support ML development

Salary

This position is full-time and includes a generous benefits package. In compliance with NYC’s Pay Transparency Act, the annual base salary range for this position is $90,000- $100,000. New York University considers factors such as (but not limited to) the scope and responsibilities of the position, the candidate's work experience, education/training, key skills, internal peer equity, as well as specific grant funding and the terms of the research grant when extending an offer.

Please note: 

• NYU McSilver is currently operating on a hybrid schedule of 3-days a week in-person. 

• This is a grant funded position with the opportunity for ongoing renewal reviewed on an annual basis. 

• This position is not eligible for Visa Sponsorship, including STEM OPT extension.

NYU is an EOE/AA/Minorities/Females/Vet/Disabled/Sexual Orientation/Gender Identity employer.

Application Instructions

Interested applicants should apply via NYU’s Interfolio system. Be sure to include a cover letter and resume with your application. Only applicants who apply via Interfolio will be considered for this position. The institute seeks to fill this position immediately.

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NYU is an Equal Opportunity Employer and is committed to a policy of equal treatment and opportunity in every aspect of its recruitment and hiring process without regard to age, alienage, caregiver status, childbirth, citizenship status, color, creed, disability, domestic violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual orientation, unemployment status, veteran status, or any other legally protected basis. Women, racial and ethnic minorities, persons of minority sexual orientation or gender identity, individuals with disabilities, and veterans are encouraged to apply for vacant positions at all levels.

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