Aline is the bridge between senior care and technology, built to strengthen connection where it matters most. Our all-in-one platform brings together sales, marketing, operations, and engagement tools, empowering senior living communities to work smarter, communicate clearly, and deliver care with heart.

Rooted in industry expertise and born from the merger of leading solutions, Aline serves as a unifying force across the senior care space. We help communities across the country streamline processes, enhance resident and family engagement, and stay aligned through every stage of care. That’s why everything we build is designed to support stronger collaboration, seamless workflows, and more meaningful experiences for residents, families, and care teams alike.

We are looking for a motivated, curious, and technically sharp Junior Software Engineer to join our engineering team. Early career candidates encouraged to apply. We are looking for a candidate with a strong foundation in data science, machine learning, or AI engineering.

You will work alongside experienced engineers and product managers to design, build, and ship AI-powered features that directly improve outcomes for senior living communities and the people they serve. From building data pipelines and training models to deploying intelligent features within Aline’s platform, you will gain hands-on experience across the full AI product lifecycle.

Responsibilities

• Design, build, and maintain scalable data pipelines to support AI and analytics use cases across Aline’s product suite.

• Develop, train, evaluate, and fine-tune machine learning models for classification, ranking, recommendation, and NLP tasks.

• Collaborate with product and engineering teams to translate business requirements into AI-driven features.

• Integrate AI/ML model outputs into production systems via APIs and microservices.

• Monitor model performance in production; implement retraining pipelines and drift detection.

• Write clean, well-documented, and testable code following engineering best practices.

• Participate in code reviews, sprint planning, and Agile ceremonies.

• Conduct exploratory data analysis (EDA) to surface insights and identify opportunities for AI-driven improvements.

• Contribute to prompt engineering and evaluation frameworks for LLM-powered product features.

• Document data schemas, model specifications, and AI feature behavior for internal and cross-team use.

• Support troubleshooting and root cause analysis for data quality and model accuracy issues.

• Stay current with advancements in AI/ML and proactively surface relevant ideas to the team

Qualifications

Education & Experience

• Bachelor's degree in Computer Science, Data Science, AI/ML, or related field, or equivalent practical experience through projects, research, internships, or professional work.

• Academic or project experience building end-to-end ML pipelines — from data ingestion through model deployment.

• Internship, research, or capstone project experience in a data-intensive or AI domain is a strong plus.

Technical Skills

• Proficiency in Python; familiarity with scientific libraries (NumPy, Pandas, Scikit-learn).

• Experience with ML frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers.

• Working knowledge of SQL and relational databases (MySQL, PostgreSQL, or similar).

• Familiarity with cloud platforms — Azure, AWS, or GCP (Azure preferred).

• Experience with data pipeline tools (Apache Airflow, dbt, Spark, or similar) is a plus.

• Understanding of REST API design and integration patterns.

• Exposure to LLMs, prompt engineering, RAG architectures, or agentic AI patterns is a strong plus.

• Familiarity with Git version control and Agile/Scrum development practices.

• Experience with containerization (Docker) and CI/CD workflows is a plus.

Soft Skills

• Strong analytical and problem-solving mindset with intellectual curiosity about data and AI.

• Clear written and verbal communication skills; ability to explain technical concepts to non-technical stakeholders.

• Self-motivated, detail-oriented, and able to manage tasks independently in a remote or hybrid environment.

• Collaborative team player who is open to feedback and continuous learning.

• Genuine interest in the mission of improving outcomes in senior care through technology