Toronto, ON (Hybrid) Full time, Founding Team Reports to: CTO

About Uniflow Uniflow is building an Artificial Research Intelligence platform - end-to-end autonomous research with full provenance, reproducibility, and proof-backed verification. We're a seed-stage startup with a working execution substrate, verification engine, and pilot studies. The concept is proven; the scale-out needs you.

The Role As

Founding Engineer - AI/ML , you will build the AI agents that drive autonomous research - from hypothesis generation through literature survey, experiment design, execution, verification, and paper drafting.

This isn't about bolting a chatbot onto existing tools. You'll design research agents that survey literature with full provenance, generate hypotheses grounded in verified findings, and produce paper drafts with auditable claim chains. Your models will operate across domains - software engineering, medical research, robotics - with auditable claim chains at every step.

What You'll Own Research agent pipeline (hypothesis experiment verification paper)

- Design and build the multi-stage agent system that orchestrates the full research lifecycle, with human-in-the-loop control at every decision point.

RAG infrastructure for literature survey with provenance tracking

- Architect the retrieval system (BM25 + vectors + re-rank) that grounds every claim in citable, versioned sources with full evidence tracking.

Claim validation and regression verification

- Build the systems that verify generated claims against source evidence, enforce regression gates, and ensure every citation is proof-backed.

What You'll Do First 90 Days

Understand the execution substrate, provenance model, and verification pipeline

Design the retrieval architecture for literature survey with citation tracking

Build initial hypothesis generation pipeline with structured output and validation gates

First Year

Ship production research agent pipeline integrated with the execution substrate





Implement regression verification system for claim validation

Build the provenance system that makes every generated claim traceable to source

Achieve measurable reduction in time-to-paper for pilot study partners

Requirements Must Have

5+ years

in ML/AI with production deployment experience

Deep expertise in LLMs

- fine-tuning, constrained decoding, prompt engineering, evaluation

RAG systems

- retrieval architectures, embedding models, re-ranking, corpus management

Python

- PyTorch/JAX, ML infrastructure (training pipelines, serving, monitoring)

Nice to Have

Background in

deep learning

beyond LLMs (sequence models, representation learning)

Experience with

scientific computing or research automation

Familiarity with

academic publishing workflows

or citation analysis

Knowledge of

reproducibility and verification

in computational research

Track record of

production ML systems

with measurable impact

Engineering Principles We value:

RFCs for big decisions

- Architectural choices are documented and reviewed

Reproducibility > speed

- We optimize for verifiability and provenance

Provenance + observability as defaults

- Every execution path is traceable and reproducible

Why Uniflow AI-native from day one

- Work at the frontier of autonomous research. Your agents don't just assist - they generate verified, reproducible scientific results.

Execution-coupled intelligence

- Your models produce outputs that run on our substrate and feed back into learning. Tight loop from generation to execution to verification.

Founding team impact

- Shape the AI architecture, agent design, and verification framework. Early equity participation reflects your role in building the company.

Compensation

Salary:

Competitive, based on experience

Equity:

Meaningful founding-team equity package

Location:

Hybrid in Toronto (2-3 days/week in-office)

Uniflow is an equal opportunity employer. We value diversity and are committed to creating an inclusive environment for all team members.

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📌 Founding Engineer - AI/ML (Toronto)
🏢 Uniflow Technologies
📍 Toronto