Applied AI Engineer

Monitor model performance in production; design retraining pipelines and drift detection strategies.

Document model methodologies, data requirements, and performance benchmarks; communicate findings to technical and non-technical stakeholders.

Ensures compliance with organizational systems, programs, training, policies, and procedures; seeks guidance as necessary.

As required

Actively supports the safety program per the Safety Manual and IIPP.

As required

Carries out all responsibilities in an honest, ethical, and professional manner.

As required

Must be able to fulfill essential job functions in a consistent state of alertness and safe manner.

As required

Handles other duties as delegated by management.

As required

This position does not have direct supervisory responsibilities.

Senior-level candidates may provide technical mentorship to Data Analysts on ML concepts and implementation.

• Bachelor’s or Master’s Degree in Computer Science, Machine Learning, Data Science, or a related field required.
• Minimum of 3-5 years of applied ML or AI engineering experience, including: Strong Python development skills (pandas, NumPy, scikit-learn; PyTorch or TensorFlow preferred) End-to-end ML pipeline development: data preparation, feature engineering, model training, evaluation, and deployment Cloud-based ML infrastructure (Azure ML, Azure AI Services, or equivalent) RESTful API development for exposing model outputs to product surfaces
• Strong Python development skills (pandas, NumPy, scikit-learn; PyTorch or TensorFlow preferred)
• End-to-end ML pipeline development: data preparation, feature engineering, model training, evaluation, and deployment
• Cloud-based ML infrastructure (Azure ML, Azure AI Services, or equivalent)
• RESTful API development for exposing model outputs to product surfaces
• Experience working in Agile teams and managing ML workstreams in Azure DevOps or Jira.
• Familiarity with LLMs, prompt engineering, or Retrieval-Augmented Generation (RAG) architectures is a strong plus.
• Knowledge of the food safety or life sciences domain is advantageous but not required.

• Strong engineering discipline with the ability to build reliable, production-grade ML systems, not just experimental models.
• Excellent problem-solving skills; comfortable operating at the boundary of research and engineering.
• Clear communication of technical approaches and model outputs to diverse audiences including Product, Sales, and Customers.
• Collaborative mindset; works effectively across Data, Engineering, and Product teams.
• Intellectually curious and committed to staying current with advances in applied AI/ML.
• High ownership mentality — takes responsibility for model quality, performance, and business outcomes.

• Standard business hours, Monday through Friday, with flexibility for model training runs and production deployments.
• Hybrid work model: combination of in-office presence in Camarillo, CA and remote work.
• Occasional travel for industry conferences or team meetings.

In general, the following physical demands are representative of those that must be met by an employee to successfully perform the essential functions of this job.

Reasonable accommodations may be made.

• This position is considered light work — exerting up to 20 pounds of force occasionally, and/or up to 10 pounds of force frequently.
• Required to have close visual acuity to perform activities such as viewing a computer screen for extended periods.
• Frequently required to maintain a static position, talk, hear, see, and perform repetitive motions.

In general, the following conditions of the work environment are representative of those an employee encounters while performing the essential functions of this job.

• Typical office or administrative work environment. Not substantially exposed to adverse environmental conditions.
• Hybrid work model: combination of in-office (Camarillo, CA) and remote work, subject to team and business needs.