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