Product Engineer (Pharma)

Basetwo•Boston, MA

2d•Onsite

About The Position

Basetwo is a fast-growing Toronto-based AI-native company transforming Pharma & Chemical manufacturing. Our platform helps manufacturers accelerate process development, scale-up and tech transfer, improve quality control, and reduce operational risk using digital twins. We work with some of the world’s leading manufacturers to bring modern AI into complex industrial environments. Our team combines deep expertise in chemical engineering, manufacturing, digital twins, and software development to solve high-impact industrial challenges. At Basetwo, you’ll work directly with customers to build and deploy solutions that meaningfully improve how critical pharmaceutical and personal care products are manufactured around the world. We’re hiring a Product Engineer to work closely with pharmaceutical and chemical manufacturing customers to deploy AI-powered solutions in real lab-scale and GXP environments. This is a highly cross-functional role that combines engineering expertise, customer engagement, data and simulation analysis, and technical problem-solving. You’ll work directly with customer stakeholders - from process engineers to plant leadership - to understand manufacturing challenges, analyze process data, develop simulation models and drive measurable improvements using the Basetwo platform. The ideal candidate is equally comfortable discussing plant or lab-scale operations with process teams, diving into process data using Python, and translating technical insights into customer value. Internally, you will function as the engineering subject matter expert and experienced voice of our customers, via demonstrated project experience across Basetwo’s key focus areas of pharmaceutical and chemical manufacturing. Externally, you will help onboard enterprise partners by working with their process engineers and other stakeholders to maximize value derived from the Basetwo platform for process development/scale up or quality control initiatives, either through troubleshooting current workflows or architecting new workflows that could bring value to the customer. In collaboration with Sales, Product and Engineering, you will also have the unique opportunity to identify new product use cases, while building and shaping the direction of the Basetwo platform.

Requirements

5–7+ years of engineering experience in pharmaceuticals and/or chemical manufacturing
Strong understanding of plant, pilot, and lab-scale pharmaceutical manufacturing processes
Passion for using Python for simulation, modelling, and process analysis
Proven ability to understand and analyze process and lab/DoE data
Strong communication skills with the ability to work directly with customer stakeholders
Proactive problem-solver with a demonstrated track record of driving continuous improvement initiatives
Experience working in cross-functional technical environments

Nice To Haves

Knowledge of enterprise historians such as OSIsoft PI, DeltaV, Wonderware, GE Proficy, or similar industrial systems is valuable

Responsibilities

Lead Customer Success Initiatives: Partner directly with pharmaceutical and chemical manufacturing customers to deploy and scale Basetwo solutions. Lead technical onboarding and implementation of digital twin projects across pilot, lab, and commercial manufacturing environments. Build trusted relationships with process engineers and scientists, operators, and technical leadership teams. Identify opportunities for process scale up/tech transfer, operational improvement, and AI-driven insights. Translate customer pain points into actionable technical solutions and product feedback. Drive successful project execution from initial discovery through deployment and measurable business impact.
Technical Problem Solving & Communication: Analyze manufacturing, process, and lab/DoE datasets to uncover process bottlenecks and optimization opportunities. Develop simulations, models, and analytical workflows using Python and the Basetwo platform. Work with industrial data systems and historians to integrate and contextualize manufacturing data. Collaborate closely with product, engineering, and machine learning teams to improve platform capabilities. Communicate technical findings clearly to both technical and non-technical stakeholders. Support troubleshooting and root-cause investigations in complex manufacturing environments. Contribute to continuous improvement initiatives across customer manufacturing operations.