Careers at IntuitionLabsBuild the systems life‑sciences teams depend on.
IntuitionLabs is an AI and software consultancy for pharmaceutical, biotech and life-sciences companies. We implement governed AI, build custom software and data systems, and work across Veeva and validated environments.
- Founded
- 2023
- Headquarters
- San Jose, California
- Clients
- Pharma, biotech and life sciences
- Practice
- AI, software, data, Veeva, validation
Open positions
Each posting describes the role, the level, where the work happens and how to apply.
8 open positions
- AI Program Lead, Biotech and PharmaCA$130,000 – CA$170,000Remote (Canada)Full-timeView and apply
- AI Solutions Engineer, Life SciencesCA$125,000 – CA$155,000Remote (Canada)Full-timeView and apply
- AI Specialist, Regulatory and Medical WritingCA$110,000 – CA$135,000Remote (Canada or United States)Full-timeView and apply
- Project Manager, Pharma and Biotech AI ProgramsCA$85,000 – CA$125,000Remote (Canada or United States)Full-timeView and apply
- AI Specialist, Discovery and Preclinical ScienceCA$95,000 – CA$115,000Remote (Canada or United States)Full-timeView and apply
- AI Integration DeveloperCA$80,000 – CA$100,000Remote (Canada)Full-timeView and apply
- Associate AI Specialist, Discovery and Preclinical ScienceCA$65,000 – CA$80,000Remote (Canada or United States)Full-timeView and apply
- Associate AI Specialist, Regulatory and Medical WritingCA$60,000 – CA$75,000Remote (Canada or United States)Full-timeView and apply
The work
Our clients run regulated operations. A useful AI assistant there may need document authority, enterprise identity, a Veeva API, data transformation, a human-review interface, validation evidence, training and ongoing support. The hard problems sit between those categories, and that is where our people work.
Engagements start from a specific business constraint rather than a service catalog, and they end with something a client team can own and operate. The disciplines below are the ones we assemble to get there.
- AI accelerationGoverned AI implemented one department at a time, with adoption support and measured results.
- Governed information layerIdentity, permissions, retrieval, citations and evaluation that connect AI to authoritative sources.
- Custom software and AIWorkflow applications, agents, automation and human-review experiences built around existing systems.
- Data engineering and BIPipelines, models, semantics, governance and analytics for decisions and AI-enabled workflows.
- Veeva servicesImplementation, extension, integration and support across commercial, clinical, regulatory and quality applications.
- Validation and assuranceRisk-based requirements, testing, traceability and change evidence under the client’s quality system.
How we work
These are the principles we apply on client engagements. They also describe the job: what you will be asked to reason about, write down and stand behind.
Start from the constraint
We begin with the workflow, system or decision that is blocking a team, and bring in only the disciplines needed to resolve it. Scopes are staged, with visible decision points.
The workflow is the unit of change
A model licence or a demo is not the outcome. The outcome is work people repeat with governed information, clear review steps, support and measured effect.
Build what someone can own
Architecture covers identity, access, data flow, logging, failure handling, support and change. A demonstration only becomes valuable when someone can operate it.
Be precise about accountability
No provider can guarantee compliance. We design proportionate controls and evidence, work alongside internal teams and vendors, and keep decisions with the client’s qualified functions.
Who we are looking for
Each posting lists its own requirements. Across roles, the people who do well here share a few traits.
- Depth in a discipline
- Software or data engineering, applied AI, Veeva configuration and development, or computer system validation. You are the person others ask when the problem gets specific.
- Respect for regulated work
- You understand, or want to learn properly, why 21 CFR Part 11, GAMP 5 and validation evidence shape how software is built and changed.
- Clear writing
- Much of the job is explaining a design, a risk or a trade-off to quality, regulatory, medical and commercial stakeholders who are not engineers.
- Judgement about AI
- You know where a language model helps and where deterministic software is the right tool, and you can show the evidence for the choice.
- Ownership after launch
- You care whether what you shipped still works, is supported and is understood months later, not only whether the demo went well.
How hiring works
Steps can vary by role. Each posting describes its own process.
Apply
Apply through the link on the posting. If no posting fits, introduce yourself through our contact page.
First conversation
A conversation about your background, the role and the kind of work you want to do next.
Working discussion
A discussion grounded in real work: a system you built, a problem you solved, or a scenario close to the role.
Decision
If there is a mutual fit, we discuss terms and confirm the offer in writing.
Not seeing the right role?
Tell us about your background and the work you want to do in life sciences.
Introduce yourself