About P&P Optica

P&P Optica (PPO) is using brand new technology to transform an old industry that affects everyone on the planet: the food industry. We apply a complex and highly sophisticated mix of hardware, software and huge datasets to improve both the safety and quality of the food we eat.

PPO is on a mission to become the world’s leading food information company. We are moving beyond simple detection to provide real-time, actionable insights that help food processors optimize yield, reduce waste, and ensure safety at a global scale.

We’re a truly diverse team in every sense of the word, and our team is incredibly smart. Our culture is respectful and caring. PPO is a well-funded, scaling company with opportunities for career growth in every part of the organization.

If you’re looking for an opportunity to tackle challenging problems that affect everyone on the planet, PPO is the place for you.

Location

This role is a hybrid role, where you’ll work primarily from PPO’s office in Waterloo Ontario. To ensure a smooth onboarding and integration, this role requires 5 days a week on-site for the first 3 months, transitioning to 3 days a week on-site on an ongoing basis. On-site days are full days during standard daytime business hours.

Compensation

The salary range for this role is $115,000 - $120,000 CAD annually. The final offer will be determined based on a combination of factors, including:

• Relevant skills and experience: We assess each candidate’s background, achievements, and demonstrated capabilities.
• Internal equity: We strive to maintain consistency and fairness across roles within our organization, aligning compensation with existing team members in comparable roles.

We are committed to transparency and fairness in our hiring process and welcome conversations about compensation throughout the recruitment process.



What You’ll Get To Do

This posting is for a current vacancy within our software team.

We are looking for a rigorous, delivery-focused Machine Learning Developer with 3 or more years of experience who thrives at the intersection of software development and machine learning models to join our team and help us build, optimize, and deploy the next generation of our predictive models.

In this role, you will be responsible for designing, training, and validating machine learning models that analyze complex hyperspectral data streams in real time. Because our systems operate inline on active food production belts, you must possess an exceptional attention to detail to ensure accuracy, and a relentless delivery focus to get models out of the lab and into the field. Working within an Agile framework, you will collaborate with software and data teams to build robust, repeatable pipelines that scale. Preference will be given to software developers who have developed or want to develop machine learning models, over data scientists who have not shipped to production.

In this role, you will work on:

• Develop ML Systems: Design, build, and scale machine learning algorithms and data pipelines from prototype to production. Perform real-time classification, segmentation, and regression tasks on hyperspectral imaging data.
• Software Engineering: Apply software best practices, including coding standards, testing, code reviews, and source control management.
• Feature Engineering: Extract, analyze, and optimize features from high-dimensional spectral datasets, ensuring models remain highly performant under strict real-time processing constraints.
• Clean Code Architecture: Write clean, maintainable, and well-tested code, ensuring that ML components seamlessly integrate with PPO’s broader software ecosystem.
• AI-First Workflow: Embrace an AI-first mindset, actively utilizing generative AI programming tools to optimize your workflow, automate routine scripting, and accelerate engineering velocity.
• Rigorous Validation: Collaborate with Data Scientists to apply statistical validation metrics to ensure model accuracy, safety, and generalization across diverse customer operational environments.
• Delivery-Focused Execution: Employ continuous delivery approaches to support iterative delivery of model enhancements, ensuring deadlines are met without compromising on quality or system stability.
• CI/CD Integration: Work closely with MLOps and DevOps to integrate model training, testing, and deployment into automated CI/CD pipelines, treating models with the s…