ML Ops Engineer

About Inspiren

Inspiren was created to help operators forge thriving senior living communities.

We use a simple, streamlined platform that protects resident privacy, to optimize community operations at every step. Our technology puts residents first, capturing insights on everything from revenue leakage to staff utilization, while providing an extra layer of oversight, as an extension of your care team.

We know that balancing operations takes time and effort, not to mention careful coordination of many parts – that’s why we offer seamless solutions to guide stronger care decisions. Because while you can’t control any specific event, we believe that data can power communities to live and work better.

Keeping your residents healthy and your staff productive is easy with Inspiren.

Smarter care, on every wall. One room at a time.

About the Role

We are seeking a highly skilled ML Ops Engineer to help create our ML pipelines for both traditional CV and LMM based models for the Inspiren platform.  You will drive innovation, ensure the integration of cutting-edge technologies, and deliver software that meets the highest standards of quality and performance across the lifecycle of all of Inspiren’s devices and platforms.

What You'll Do

• Lead ML Ops Projects: Oversee the end-to-end development and deployment of machine learning models and infrastructure, from conceptualization to production and continuous improvement.
• Collaborate Cross-Functionally: Work closely with data scientists, software engineers, product managers, DevOps teams, and other stakeholders to define and implement scalable ML pipelines and infrastructure aligned with product needs.
• Innovate and Optimize: Stay current with industry trends and emerging technologies in machine learning operations. Introduce new methodologies, tools, and technologies to enhance performance and streamline workflows. Provide technical expertise in ML model deployment, monitoring, and optimization.
• Embed Rigorous Design for Excellence (DfX) Mindset: Conduct infrastructure reviews and failure mode effect analysis (FMEA). Partner with cross-functional teams to drive rigorous DfX (design for scalability, reliability, performance, and cost-efficiency) methodologies across all phases of ML pipeline development.
• Mentor Team Members: Provide technical guidance and mentorship, fostering a culture of excellence, innovation, and continuous learning.
• Ensure Quality, Reliability, and Compliance: Establish and oversee best practices for model validation, monitoring, and performance tracking. Ensure deployed ML models meet regulatory standards, ethical AI principles, and industry best practices.
• Problem-Solve: Troubleshoot complex ML pipeline issues and implement effective solutions in a timely manner. Act as Tier-2 engineering support for ML systems in production.
• Strategic Planning: Contribute to the long-term ML roadmap, aligning development with the company’s product and platform roadmap.

Qualifications

• Educational Background: Bachelor's or Master's degree in Computer Science, Data Science, Software Engineering, or a related field.
• Experience: 5+ years of hands-on experience in ML Ops, having successfully launched and managed multiple machine learning projects in production.
• Technical Expertise:
  • Programming Proficiency: Expertise in Python and familiarity with ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
  • ML Pipeline Tools: Experience with tools like MLflow, Kubeflow, Airflow, or similar workflow orchestration tools.Cloud Platforms: Proficiency in cloud platforms such as AWS, GCP, or Azure, particularly in AI/ML services and infrastructure management.
  • Containerization and Orchestration: Hands-on experience with Docker, Kubernetes, and CI/CD pipelines.
  • Data Engineering: Familiarity with data pipelines, ETL processes, and tools such as Apache Spark, Kafka, or Snowflake.
  • Model Monitoring and Optimization: Expertise in monitoring model performance and implementing automated retraining workflows.
  • Security Principles: Understanding of data security and privacy best practices in the context of machine learning.
  • Development Processes: Well-versed in Agile/Scrum methodologies and MLOps best practices.
• Communication: Excellent verbal and written communication skills, with the ability to convey complex ideas clearly.
• Adaptability: Comfortable working in a fast-paced, dynamic environment and adapting to changing priorities.
• Start-up experience is a plus.

Details

• The annual salary for this role is between $170,000-200,000 + equity + benefits (including medical, dental, and vision) 
• Flexible PTO
• Location: Remote, US
• Join our team and make a meaningful impact on patient care by enabling healthcare organizations to adopt and leverage AUGi to its full potential. Apply today to become a part of our customer success team!
• Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, or disability status.