Senior Staff Machine Learning Engineer 2

Overview
At PowerSchool, we are a dedicated team of innovators guided by our shared purpose of powering personalized education for students around the world. From the central office to the classroom to the home, PowerSchool supports the entire educational ecosystem as the global leader of cloud-based software for K-12 education. Our employees make it all possible, and a career with us means you're joining a successful team committed to engaging, empowering, and improving the K-12 education experience everywhere.

Team Overview
Our Research & Development (R&D) team is the technical talent at the heart of our product suite, overseeing the product development lifecycle from concept to delivery. From engineering to quality assurance to data science, the R&D team ensures our customers seamlessly use our products and can depend on their consistency.

This position, under the general direction of the Lead and/or Manager, Machine Learning Engineering, will be responsible for technical and development support for our award-winning K-12 software. This role will help in all AI/Generative AI/Agents products in the areas of engineering, data, deployment and infrastructure .

Responsibilities
Essential duties and responsibilities include the following. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions

• Uses Generative AI models (GPT4, Claude, Gemini), other LLMs, Agents and LangChains, CrewAI, Strands to build different AI driven smart solutions
• Experience in building GenAI based complex workflow in auto-pilot or co-pilot mode which run in production
• Experience in building Responsible AI guardrails and metrics around traditional AI or Gen AI models
• Familiar with latest Gen AI, Agent based products and AI coding tools in the market
• Fundamental understanding of NLP, transformer and embedding space
• Design and implement Machine learning models and data ingestion pipelines
• Experience in building software products used in production
• Create and maintain optimal data pipeline architecture by assembling large, complex data sets to meet functional and non-functional business requirements
• Identify and implement internal process improvements including automating manual processes, optimizing data delivery, and redesigning infrastructure for greater scalability
• Support the building of machine learning, data platforms, and infrastructure required for optimal data extraction, transformations, and loading of data from a wide variety of data sources
• Work with architecture, data, and design teams to assist with data related technical issues and support data infrastructure needs
• Deploy ML models in AWS environment specifically in AWS Sage Maker environment
• Implement Model Monitoring, Data Quality Checks, Data Drifts in Inference Pipelines
• Support ML teams in the delivery of continuous integration, continuous deployment, providing templates and patterns
• Perform root cause analysis for production issues where the root cause is in infrastructure, environment, configuration, or deployment routines; understand when to escalate to product development teams; remediate root causes and implement preventative actions
• Own the AWS stack which comprises all ML resources and collaborate on managing ML infrastructure costs
• Establish standards and practices around MLOps, including governance, compliance, and data security
• Uses customer management system to provide status on open customer issues and properly verifies when an issue can be closed

Qualifications
To be considered for and to perform this job successfully, an individual must be able to perform each essential duty and responsibility satisfactorily. The requirements listed below are representative of the knowledge, skill and/or ability required.

• Qualifications include:

• At least 5+ years of experience within the full software development lifecycle from planning through deployment and maintenance

• You're a highly technical research engineer with a strong understanding of the latest advancements in AI, especially GenAI and LLMs.
• You have 5+ years of professional experience in software engineering, machine learning, or applied research, with a proven ability to drive high-impact AI initiatives end to end.
• Strong working knowledge of deep learning, machine learning and statistics
• Experience related to AWS services such as SageMaker, Bedrock, EMR, S3, OpenSearch Service, Step Functions, Lambda, and EC2
• Hands on experience with deep learning (e.g., CNN, RNN, LSTM, Transformer), machine learning, CV, GNN, or distributed training
• Strong communication skills, with attention to detail and ability to convey rigorous mathematical concepts and considerations to non-experts
• Demonstrated ability to design, implement, and scale machine learning workflows (ML OPs); including deployment and delivery of production-ready model APIs
• Proficiency with at least one machine learning lifecycle platform (Sagemaker, MLFlow, TensorFlow, etc.), orchestration platform (Airflow, Dagster, etc.) and data platform like SnowFlake/DataBricks
• You have a track record of implementing cutting-edge research into robust, scalable, and well-tested code.
• You bring a strong engineering mindset and write clean, efficient code that performs reliably in production.
• You've had some experience mentoring or guiding others—formally or informally—and are excited by the opportunity to help shape a new team.
• You measure the impact of your work and provide clear visibility into what you're building and why it matters.
• Experience working with ML engineers to build tooling and automation to support the entire ML engineering lifecycle, from experimentation to production operations
• Experience with Kubernetes and ML CI/CD workflows
• 3+ years experience with AWS or other public cloud platforms (GCP, Azure, etc.)
• Excellent verbal and written communication skills.
• Experience with Infrastructure-as-Code tools and frameworks
• Bachelor's degree in computer science, data science, mathematics, or a related field. Master's degree preferred

EEO Commitment
EEO Commitment
PowerSchool is committed to a diverse and inclusive workplace. PowerSchool is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. Our inclusive culture empowers PowerSchoolers to deliver the best results for our customers. We not only celebrate the diversity of our workforce, we celebrate the diverse ways we work. If you have a disability and need an accommodation regarding our recruiting process, please let us know by emailing