ML Ops Engineer

December 20, 2024

🏄 California – Remote

Although this job is listed in California, this company may be open to hiring remote in other states.

🐊 Florida – Remote

Although this job is listed in Florida, this company may be open to hiring remote in other states.

+6 more states

🏄 California – Remote 🐊 Florida – Remote 🌽 Illinois – Remote 🗽 New York – Remote 🍂 Massachusetts – Remote 🔔 Pennsylvania – Remote 🤠 Texas – Remote ☕ Washington – Remote

💵 $170k - $200k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

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Inspiren

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Inspiren is a comprehensive solution for managing and optimizing operations in senior living communities. The platform enhances resident care by providing decision-grade data analysis for care planning, staffing optimization, and resident safety. Inspiren uses advanced technology, such as AUGi, to enable proactive care, improve workflow efficiency, and align care plans with resident needs. The technology's capabilities include real-time alerts for incidents and early risk flagging, tracking resident interactions, and monitoring staff effectiveness, thus ensuring high-quality resident care and operational efficiency.

2 - 10 employees

⚕️ Healthcare Insurance

☁️ SaaS

🧘 Wellness

💰 $2.7M Venture Round on 2022-10

📋 Description

• Inspire was created to help operators forge thriving senior living communities. • We offer seamless solutions for stronger care decisions. • We are seeking a highly skilled ML Ops Engineer to create ML pipelines for traditional CV and LMM models. • You will drive innovation, ensure integration of cutting-edge technologies, and deliver software meeting the highest standards.

🎯 Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Software Engineering, or a related field. • 5+ years of hands-on experience in ML Ops, having successfully launched and managed multiple machine learning projects in production. • Expertise in Python and familiarity with ML frameworks like TensorFlow, PyTorch, or Scikit-learn. • Experience with tools like MLflow, Kubeflow, Airflow, or similar. • Proficiency in cloud platforms such as AWS, GCP, or Azure in AI/ML services. • Hands-on experience with Docker, Kubernetes, and CI/CD pipelines. • Familiarity with data pipelines, ETL processes, and tools like Apache Spark, Kafka, or Snowflake. • Expertise in monitoring model performance and implementing automated retraining workflows. • Understanding of data security and privacy best practices in ML. • Well-versed in Agile/Scrum methodologies and MLOps best practices. • Excellent verbal and written communication skills. • Comfortable working in a fast-paced environment and adapting to changing priorities. • Start-up experience is a plus.

🏖️ Benefits

• Equity • Medical • Dental • Vision • Flexible PTO