Founding Engineer - AI/ML

About Uniflow

Uniflow is building an AI-native business operating system—a complete platform for enterprise software that combines deterministic execution with governed AI. We're a seed-stage startup with a working compiler, runtime, and reference implementations. The concept is proven; the scale-out needs you.

The Role

As Founding Engineer - AI/ML, you will build the AI layer that transforms business requirements into runnable software—learning from semantic models and runtime execution to compress enterprise delivery cycles.

This isn't about bolting a chatbot onto existing software. You'll design generation models that emit validated, deployable artifacts directly integrated with our runtime. You'll build deep learning pipelines that extract business insights from execution signals. And you'll architect the retrieval and governance infrastructure that makes AI-generated outputs auditable and enterprise-safe.

What You'll Own

Generation model (requirements → validated, deployable artifacts) — Design and train models that take business requirements and produce configuration or code that compiles, validates, and runs on our platform. Constrained decoding, schema-aware generation, fail-closed validation.
Deep learning for business insights from runtime signals — Build pipelines that learn from entity lifecycle execution, workflow patterns, and operational telemetry to surface predictions, recommendations, and optimization opportunities.
RAG infrastructure with governed retrieval + audit trails — Architect the retrieval system (BM25 + vectors + re-rank) that grounds generation in licensed, versioned corpus with full evidence tracking for compliance and auditability.

What You'll Do

First 90 Days

Understand the business domain model, entity semantics, and runtime execution patterns
Design the retrieval architecture for governed, evidence-tracked generation
Build initial generation pipeline with constrained decoding and validation gates
Establish evaluation framework (pass rates, compile success, policy conformance)

First Year

Ship production generation model integrated with our platform tooling
Implement deep learning pipeline for runtime signal analysis and business insights
Build the audit/evidence system that makes every AI output traceable to source
Achieve measurable cycle-time compression for enterprise configuration changes

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)
Experience with structured generation (grammar-constrained, schema-aware output)

Nice to Have

Background in deep learning beyond LLMs (sequence models, representation learning)
Experience with enterprise software or business process automation
Familiarity with compilers or DSLs (helpful for understanding our domain)
Knowledge of compliance/audit requirements in enterprise AI
Track record of production ML systems with measurable business impact

Engineering Principles

We value:

RFCs for big decisions — Architectural choices are documented and reviewed
Correctness > cleverness — We optimize for maintainability and verifiability
Determinism + observability as defaults — Every execution path is traceable and reproducible

Why Uniflow

AI-native from day one — Work on problems that haven't been solved at scale. Our AI doesn't just assist—it generates validated, runnable business software.
Runtime-coupled generation — Your models emit artifacts that compile and run on our platform. Tight feedback loop from generation to execution to learning.
Founding team impact — Shape the AI architecture, training methodology, and governance framework. Early equity participation reflects your role in building the company.

Compensation

Salary: Competitive, based on experience
Equity: Meaningful founding-team equity package
Benefits: Health, dental, vision (Canada)
Location: Hybrid in Toronto (2-3 days/week in-office)

How to Apply

Send your resume and a brief note on why this role interests you to careers@uniflow.tech or apply through our website at uniflow.tech.

Include any relevant:

Papers, blog posts, or projects demonstrating ML/AI work
Production systems you've shipped and their impact
Examples of structured/constrained generation work
Thoughts on how AI should work in enterprise software

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