Technical Problem-Solver & Customer Success Engineer (AI Tools Focus)

This is a high-impact role that helps define how CoLab delivers technical value to its most strategic customers. You’ll act as a dedicated technical problem-solver and trusted advisor, embedding with enterprise customers to optimize how they use CoLab—especially our AI tools.

You’ll help large mechanical engineering orgs rethink how they manage feedback, data, and decision-making across design and PLM (Product Lifecycle Management). That means getting deep into customer workflows, testing new ways of working, and building repeatable methods the rest of the team can scale.

You’ll work within our forward-deployed team, alongside a technical program manager, software FDE, and customer-facing teams to close technical gaps, shape product direction, and directly influence customer success. This role isn’t about selling or pitching—it’s about deeply understanding engineering problems and solving them alongside the customer.

Job Responsibilities

• Embed with enterprise customers to optimize CoLab usage, with a focus on AI tools like AutoReview and Operator.
• Investigate and solve complex engineering data and integration challenges across PLM, ERP, QMS, and traditional design feedback tools.
• Partner with a cross-functional team to test and implement new CoLab workflows inside customer environments.
• Collaborate with product teams to identify feature gaps, improve capabilities, and refine AutoReview.
• Build technical documentation, playbooks, and repeatable frameworks based on successful projects.
• Provide mechanical engineering expertise to validate AI-generated results and ensure engineering rigor.

Qualifications

Must-haves:

• Background in mechanical design engineering, ideally in a medium to large-sized organization.
• Strong understanding of PLM systems and enterprise engineering data architecture.
• Demonstrated ability to lead technical projects and cross-functional collaborations.
• Experience working directly with customers or internal stakeholders to solve technical problems.
• Comfort working in fast-changing, ambiguous environments.
• Strong written communication and documentation skills.
• Working knowledge of high-level AI architecture, specifically LLMs (Large Language Models).
• Familiarity with APIs, data integrations, and enterprise architecture concepts.

Nice-to-haves:

• Experience with internal tool development, scripting, or automation.
• Exposure to AI/ML tools applied to engineering workflows.
• Prior work in design consulting or product development at scale.