Senior Machine Learning Engineer

Overview

At Power School, 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, Power School 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.

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.

• Uses Generative AI models (GPT‑4, Claude, Gemini), other LLMs, Agents and Lang Chains, CrewAI, Strands to build different AI and agent‑driven intelligent solutions
• Experience in building GenAI‑based solutions in auto‑pilot or co‑pilot mode that run in production at scale
• Experience in building Responsible AI guardrails and metrics around traditional AI or GenAI models
• Familiar with latest GenAI, agent‑based products and AI coding tools in the market
• Fundamental understanding of NLP, transformer, embedding space and evaluation metrics
• Design and implement fine tuning SLMs, core machine‑learning models and data ingestion pipelines
• Experience in dealing with “messy” enterprise data and building ETL transformations to facilitate more effective AI decision‑making
• 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
• 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
• Perform root‑cause analysis for production issues where the root cause is in infrastructure, environment, configuration or deployment routines; understand when to escales to product development teams; remediate root causes and implement preventative actions
• Establish standards and practices around MLOps, including governance, compliance and data security
• Experience with AI or agent‑driven complex ETL generation and data transformation preferred

• You’re a highly technical research engineer with a strong understanding of the latest advancements in AI, especially GenAI – LLMs and agents
• You have 5+ years of professional experience in software engineering, GenAI, 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
• Some experience in complex SQL and ETL transformations
• Experience related to AWS services such as Sage Maker, Bedrock, EMR, S3, Open Search 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 (MLOps), including deployment and delivery of production‑ready model APIs
• Proficiency with at least one machine‑learning lifecycle platform (Sage Maker, MLflow, Tensor Flow, etc.), orchestration platform (Airflow, Dagster, etc.) and data…