What are the responsibilities and job description for the ML Ops Engineer position at Inspiren?
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
Programming Proficiency : Expertise in Python and familiarity with ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
Details
Salary : $170,000 - $200,000
