Join us

What you'll do

• Own the full sales cycle: outreach, qualification, demos, negotiation, close
• Turn early founder-led learnings into a repeatable sales process
• Build relationships with technical buyers—VPs Engineering, CTOs, data and ML leads
• Shape pricing, packaging, and positioning as we learn what resonates
• Hire and lead a sales team as we grow

You might be a good fit if you

• Have sold technical products to technical buyers (data infrastructure, developer tools, B2B SaaS)
• Are comfortable with early-stage ambiguity—you'll define this role as much as fill it
• Can have credible conversations about data, APIs, and ML without reading from a script
• Want to build something, not just execute a playbook someone else wrote
• Are based in Toronto or willing to relocate

Why this role is interesting

The product sells—founders just can't scale it. You'll inherit real customers, real feedback, and a technical product with genuine differentiation. This is a ground-floor opportunity to build the commercial side of the company.

What you'll work on

• Agent systems that combine LLMs with structured data access (SQL, vector search, knowledge graphs)
• Multi-provider LLM orchestration with cost-aware routing and caching
• Embedding pipelines that scale—batching, CPU/GPU routing, model selection
• Structured prediction pipelines for domain-specific tasks
• The intersection of ML and data infrastructure: making models useful over real datasets

You might be a good fit if you

• Have built agent systems, RAG pipelines, or LLM-powered applications in production
• Care about data quality as much as model quality
• Are comfortable in Python, and curious about systems-level optimization
• Want to work on the hard parts: reliability, evaluation, cost, latency—not just demos
• Get excited about making AI useful for domain experts, not just other engineers

Why this role is interesting

You'll work on real datasets—millions of patents, regulatory filings, financial disclosures—building agents that help domain experts find what matters. This isn't wrapper-over-ChatGPT work; it's making AI reliable enough that people trust it for real decisions.

What you'll work on

• Query engine development in Rust: optimizers, execution strategies, custom operators
• Storage for ML-native data: embeddings, vectors, images, audio, unstructured files
• Data ingestion at scale: streaming, schema evolution, catalog management
• Performance: caching strategies, I/O optimization, memory management
• Extending SQL to do more: vector search, similarity joins, ML-aware query planning

You might be a good fit if you

• Love building data systems—query planners, storage engines, serialization formats
• Are strong in Rust and enjoy working close to the metal
• Care about performance: you've profiled queries, tuned caches, or optimized I/O
• Are excited by the intersection of data infrastructure and machine learning
• Want to build something new, not maintain something old

Why this role is interesting

Most query engines treat ML as an afterthought—embeddings stored separately, inference done outside the database. We're building infrastructure where vectors, images, and unstructured data are first-class citizens, queryable with SQL alongside structured data. It's a hard problem and we're early.