Machine Learning Engineer (AI)

Location: San Diego, CA, United States

Salary: $117,000–$175,000 USD annually

Company

hirify.global develops healthcare technologies and applies AI and machine learning to improve patient outcomes, support personalized recommendations, analyze sleep data, identify health risks, and address operational challenges.

What you will do

• Design, build, test, operate, and support production machine learning components and services.
• Build pipelines covering data preparation, feature engineering, training, evaluation, deployment, monitoring, and retraining.
• Develop APIs and services that integrate machine learning into web, mobile, and backend applications.
• Train, evaluate, deploy, monitor, and troubleshoot models and production ML systems for data quality, performance, reliability, scalability, and cost.
• Collaborate with Data Scientists, ML Engineers, Data Engineers, Software Engineers, and Product partners to move prototypes and requirements into production.
• Communicate technical trade-offs, contribute to engineering standards and documentation, and support less-experienced colleagues.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics, or a related technical field, or equivalent practical experience.
• 1–2 years of professional experience in Machine Learning Engineering, Data Engineering, Software Engineering, or a similar role, including production ML experience or equivalent demonstrated experience.
• Working knowledge of the production ML lifecycle, including data and feature pipelines, training, evaluation, inference, APIs, deployment, continuous training, and monitoring.
• Strong Python and software-engineering fundamentals, including pandas, NumPy, and scikit-learn.
• Experience with relational SQL databases and at least one NoSQL or distributed data technology.
• Hands-on experience training, evaluating, and serving ML models through APIs or batch-processing services.
• Experience with workflow orchestration such as Airflow, a major cloud platform such as AWS, Docker, and Git-based CI/CD.
• Ability to monitor and troubleshoot production services, pipelines, and model behaviour.
• Clear written and verbal communication with technical and non-technical audiences.

Nice to have

• Experience with MLOps platforms such as Amazon SageMaker or MLflow, Kubernetes or Amazon EKS, Snowflake, Kafka, or Flink.
• Familiarity with model explainability, responsible AI, bias, or fairness assessment.
• Familiarity with large language models, retrieval-augmented generation, LangChain, or LangGraph.
• Experience in healthcare, medical devices, or privacy-sensitive data environments.

Culture & Benefits

• Work in a cross-functional AI Engineering environment focused on ownership, practical judgement, collaboration, clarity, and continuous improvement.
• Medical, dental, vision, life, AD&D, and disability insurance.
• HSA, FSA, commuter benefits, sleep care management, 401(k), ESPP, EAP, and tuition assistance.
• Flex time off for salaried employees, 11 paid holidays, and 3 floating holidays.
• Up to 14 weeks of primary caregiver leave or 2 weeks of secondary caregiver leave.