Role overview
A Place for Mom is the leading platform guiding families through every stage of the aging journey. Together, we simplify the senior care search with free, personalized support — connecting caregivers and their loved ones to vetted providers from our network of 15,000+ senior living communities and home care agencies.
Since 2000, our teams have helped millions of families find care that fits their needs. Behind every referral and resource is a shared goal: to help families focus on what matters most — their love for each other.
We’re proud to be a mission-driven company where every role contributes to improving lives. Caring isn’t just a core value — it’s who we are. Whether you’re supporting families directly or driving innovation behind the scenes, your work at A Place for Mom makes a real difference.
Our employees live the company values every day:
- Mission Over Me: We find purpose in helping caregivers and their senior loved ones while approaching our work with empathy.
- Do Hard Things: We are energized by solving challenging problems and see it as an opportunity to grow.
- Drive Outcomes as a Team: We each own the outcome but can only achieve it as a team.
- Win The Right Way: We see organizational integrity as the foundation for how we operate.
- Embrace Change: We innovate and constantly evolve.
What you'll work on
Machine Learning Model Development and Maintenance:
- Study and transform data science prototypes using appropriate ML and GenAI architectures.
- Design and develop machine learning and LLM-powered systems using modern GenAI architectures (e.g., RAG, prompt engineering, embeddings, vector databases).
- Solve complex problems with multilayered data sets and optimize existing machine learning libraries and frameworks.
- Construct optimized data pipelines to feed both traditional ML models and LLM-based systems.
- Run machine learning tests and experiments and document findings and results.
Model Maintenance and Monitoring:
- Implement and monitor model and data quality checks to ensure accuracy and consistency of our models and pipelines in production.
- Ensure system reliability, performance optimization, and responsible deployment of ML and LLM solutions in production.
- Manage the full model lifecycle, including retraining, performance monitoring, and continuous improvement.
Collaboration and Support:
- Partner with Product, Engineering and Business stakeholders to translate requirements into scalable AI/ML and GenAI solutions, contributing to system architecture and technical design.
- Provide technical guidance and support on AI/ML initiatives, delivering clear, actionable insights to stakeholders.
- Contribute to AI engineering standards and best practices across the organization.
Best Practices and Documentation:
- Create and maintain comprehensive documentation for ML and GenAI systems, including prompt libraries, evaluation frameworks, and architectural decisions.
- Establish and promote best practices across MLOps, LLMOps and AI system governance.
What we're looking for
Master’s degree in Computer Science, Mathematics, or a related field, or equivalent working experience.5+ years of proven experience as a Machine Learning Engineer, with significant experience working with data from structured and unstructured data sources, ETL processes, and data quality management.
Strong proficiency in SQL, Databricks, AWS services, Python, and Spark.
Experience with ML frameworks such as XGBoost, Scikit-learn, TensorFlow, Keras, or PyTorch.
Hands-on experience building and deploying LLM-powered applications, including prompt engineering, evaluation frameworks, Retrieval-Augmented Generation (RAG), embedding models/vector databases.
Familiarity with MLOps and/or LLMOps tooling and CI/CD workflows.
Excellent analytical and problem-solving skills with a strong ability to derive insights from complex data sets.
Effective communication skills with the ability to convey technical information and business impact to non-technical stakeholders.