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Senior AI Engineer

  • Engineer, BC, Canada

Full-time

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Job details

Job type
Full-time
Apply by
Experience
At least 4+ years of professional engineering experience, with broad full-stack range
Posting language
English
Posted

Original job description

The highest calling for AI is improving human health. At Perceptic, we're building the AI operating system that powers drug discovery - a unified intelligence layer that connects evidence, reasoning, and action across the entire drug lifecycle.

We were built by operators from Palantir who spent years deploying production AI in the world's most demanding enterprise environments. We know what it takes to make ambitious technology hold up under real pressure. Now we're applying that to pharma and we're hiring engineers who've operated at the same standard.

We are hiring Senior AI Engineers who have shipped real AI product experiences. We mean people who have built AI products, broken them in production, fixed them, and shipped them to users who depend on the result. Curiosity about AI isn't enough.

The hard problem here is getting AI to reason over the world's scientific evidence well enough that a pharma scientist will stake a major decision on the result. That takes agents doing substantive work, surfaced through interfaces that make their reasoning easy to follow and check.

Our stack is React and TypeScript on the front end, Java and Python services behind it, and an agent layer built on durable, fault-tolerant workflows that reason over scientific data through a growing set of tools and integrations. We keep the technology choices few and high-leverage, and you'll work across all of them.

What You'll Own

  • End-to-end product features across the frontend, backend, data, and API layers, owned through to production
  • AI-powered scientific workflows: agentic systems, retrieval, tool use, and the evaluation loops that keep them honest
  • The integration work that turns model providers and AI tooling into product experiences people can depend on
  • Evaluation and feedback systems that make AI behavior measurable and improvable over time
  • Trust surfaces such as provenance, citations, and confidence, rendered so a scientist can check the work behind any answer
  • Close work with founders, customers, and domain experts, turning ambiguity into shipped product
  • Engineering velocity through automation, AI-assisted development, and internal tooling that lets a small team do the work of a much larger one

What We're Looking For

  • At least 4+ years of professional engineering experience, with broad full-stack range
  • You've shipped AI features that people rely on, and you can point to them
  • You can talk concretely about harnesses, evals, failure modes, latency, cost, and the UX tradeoffs of building with language models
  • You use AI coding tools fluently and have strong, earned opinions about where they help and where they fall down
  • You've come from somewhere that held a high bar, such as FAANG, a top AI lab, or a respected product-engineering environment
  • You want more ownership, speed, and upside than those environments tend to offer
  • You have strong product taste and can make pragmatic calls without waiting for perfect requirements

This is a high-intensity, high-stakes role. We move quickly and give direct feedback, the responsibility you carry will likely outrun your title, and you'll go wherever the company needs you most, which sometimes means unglamorous work that matters more than anything else that week. The people who do well here find that kind of ownership and growth exciting, and they weren't looking for a nine-to-five.
More about this job

Responsibilities

Build and ship end-to-end AI-powered scientific product features across frontend, backend, data, and API layers, including agentic workflows, retrieval, integrations, and evaluation systems. Develop trustworthy interfaces that expose provenance, citations, and confidence, while working with founders, customers, and domain experts to turn ambiguous needs into production products.

Requirements

Applicants need at least four years of professional engineering experience, broad full-stack capability, and demonstrated experience shipping AI features that users rely on. They should be able to discuss AI evaluation, failure modes, latency, cost, and user experience tradeoffs, use AI coding tools fluently, and bring strong product judgment from a high-bar engineering environment.

Skills

  • Full-Stack Engineering
  • Artificial Intelligence
  • React
  • TypeScript
  • Java
  • Python
  • Agentic Systems
  • Retrieval
  • Tool Integration
  • AI Evaluation
  • Workflow Engineering
  • API Development
  • Data Engineering
  • AI-Assisted Development
  • Product Development
  • Scientific Data

Visa sponsorship

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Categories

  • Technology
  • Software
  • Engineering
  • Healthcare
  • Science & Research

Keywords

  • Artificial Intelligence
  • Drug Discovery
  • Pharmaceuticals
  • React
  • TypeScript
  • Java
  • Python
  • Full-Stack Engineering
  • AI Agents
  • Agentic Systems
  • Retrieval
  • Scientific Evidence
  • Scientific Data
  • Durable Workflows
  • Fault-Tolerant Workflows
  • Model Providers
  • AI Tooling
  • Evaluation Harnesses
  • Feedback Systems
  • Provenance
  • Citations
  • Confidence
  • API Development
  • Production AI
  • AI-Assisted Development
  • Automation
  • Internal Tooling
  • Latency
  • Cost Optimization
  • Product Engineering

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