Staff Machine Learning Engineer

Company Description

About A Place for Mom:

A Place for Mom is the leading online resource connecting families searching for senior care with a team of expert advisors providing insight-driven, personalized solutions. As the nation’s largest senior care advisory service, A Place for Mom helps hundreds of thousands of families every year navigate the complexities of finding the right senior care solution for their loved ones across home care, independent living, memory care, assisted living, and more. Established in 2000 as a family business, A Place for Mom employees are deeply committed to the company mission to enable caregivers to make the best senior care decisions. A Place for Mom fosters, cultivates, and preserves a culture of diversity, equity, and inclusion.

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.

Job Description

A Place for Mom is looking for a Staff Machine Learning Engineer to help build practical, data-driven machine learning applications. In this role, you will architect and deploy scalable ML systems, working closely with data scientists, machine learning engineers, and other cross-functional teams to create real-world applications powered by AI. You should be comfortable digging into large datasets, using your background in statistics and programming to find patterns and build models that work in production. We're also looking for someone who can help improve and fine-tune our existing algorithms to make them faster, more accurate, and more reliable.

Key Responsibilities and Deliverables:

• Technical Leadership and Mentoring:
  • Own and drive initiatives from idea to production, managing cross-functional stakeholders.
  • Drive architecture decisions and influence long-term strategy
  • Provide technical guidance and mentorship to a team of data scientists and machine leaning engineers
• Machine Learning Model Development and Maintenance:
  • Own the full ML lifecycle: from data exploration to model development, deployment, and continuous optimization.
  • Deep understanding of real-time inference systems, streaming data pipelines, model serving services, feature store monitoring and latency optimization for ML & LLM applications
  • Solve complex problems with multilayered data sets and optimize existing machine learning libraries and frameworks.
  • Run machine learning tests and experiments, and document findings and results.
• Data Pipeline Development and Maintenance:
  • Architect, build, and maintain robust and scalable data pipelines to support ML model training and inference.
  • Ensure data quality, integrity, and availability for all machine learning initiatives.
• Model Maintenance and Monitoring:
  • Implement and monitor model and data quality checks to ensure accuracy and consistency of our models and pipelines in production.
  • Train, retrain, and monitor machine learning systems and models as needed.

• Collaboration and Support:
  • Collaborate with cross-functional teams, including data engineers, data scientists, machine learning engineers and product managers, to build machine learning applications that align with business objectives and best practices.
  • Provide support on model and data-related issues and queries from other teams including providing clear and actionable reporting for stakeholders.
• Best Practices and Documentation
  • Define and promote best practices for model training, evaluation, deployment, monitoring and continuous improvement

Qualifications

• Experience: 8+ years of experience in machine learning, with at least 2 years in a senior or staff-level ML engineering role, with a proven track record of delivering ML models to production with measurable impact.
• Technical Skills: Expertise in SQL, Databricks, AWS services, Python, Spark and machine learning frameworks such as XGBoost, Scikit, TensorFlow, Keras or PyTorch. Experienced in developing, deploying, and serving scalable LLM applications in production environments.
• Analytical Skills: Excellent analytical