A fast-growing startup is to work with life sciences customers to support the deployment of AI-driven solutions in technical and operational environments.

This is a cross-functional role that combines engineering, customer collaboration, data analysis, and technical problem-solving. You'll work with a range of stakeholders to understand operational challenges, analyze process and production data, develop models and workflows, and help drive improvements using the company's platform. If you are excited about being at the frontier of innovation in the application of AI, read on.

Internally, you'll act as a technical subject matter expert and advocate for customer needs across manufacturing and process-related initiatives.

Externally, you'll support enterprise customers by partnering with engineering and operations teams to maximize value from the platform, whether by improving existing workflows or helping implement current solutions.

In collaboration with Sales, Product, and Engineering, you'll also contribute to identifying new use cases and shaping future platform capabilities.

Responsibilities Customer Engagement & Project Delivery Partner with customers to support deployment and adoption of platform solutions

Lead onboarding and implementation efforts across technical and operational environments

Build relationships with engineering, operations, and leadership stakeholders

Identify opportunities for operational improvement and data-driven insights

Translate customer challenges into actionable technical solutions and product feedback

Support projects from discovery through implementation and impact measurement

Technical Analysis & Problem Solving Analyze operational and process datasets to identify trends, bottlenecks, and optimization opportunities





Develop analytical models, simulations, and workflows using Python and related tools

Work with industrial data systems and manufacturing datasets

Collaborate with product and engineering teams to improve platform functionality

Communicate technical findings to both technical and non-technical audiences

Support troubleshooting and continuous improvement initiatives in complex operational settings

Qualifications 7-10 years of experience in manufacturing, process engineering, or related industrial environments, with strong understanding of biologics pharma manufacturing

Experience building models from scratch, not just black-box development

Experience in dynamic modelling, hybrid modelling is essential to be successful

Understanding of production, pilot, or laboratory-scale operations

Required Skills Experience using Python or similar tools for analysis, modelling, or automation

Ability to work with operational and process data

Strong communication and stakeholder management skills

Problem-solving mindset with experience driving technical or operational improvements

Experience working in cross-functional technical teams

Experience with Aspen, Python or related modelling tools for mechanistic modelling

Experience in pharmaceutical manufacturing execution systems (MES)

Experience such as the below in highly relevant to the position Bioprocess scale-up; Cell Culture Modeling

Cell culture modeling

CHO cell modeling

Bioreactor modeling

Fermentation modeling

Bioprocess modeling

Upstream process modeling

Metabolic modeling

Metabolic flux analysis (MFA)

Flux balance analysis (FBA)

Genome-scale metabolic models (GEMs)

Media optimization modeling

Nutrient consumption modeling

Product quality modeling

Cell growth kinetics

Mechanistic process understanding

📌 Principal Product Engineer, Pharma or Chemical Manufacturing (Toronto)
🏢 Optimyze1
📍 Toronto