An AI transformation firm that ships working systems.
Catalyst·Wayfare is an AI transformation firm that builds production AI systems for mid-market enterprises — with a specialty helping those in regulated and technical domains. We do not stop at PowerPoint AI strategy. We ship working systems alongside our clients' engineering teams.
The team's backgrounds span MIT, McKinsey, the White House, and some of the most trusted names in AI.
Our signature platform, productized.
Trust.ML is an emerging platform offering for our firm. Where the advisory practice goes deep with a handful of clients per year, Trust.ML is the SaaS surface that extends the same governance, evaluation, and deployment discipline to organizations that are not ready or able to engage us bespoke.
It is a nascent product born of the necessity of our clients. It is in active and successful use and is ready to grow: specifically with a dedicated engineer who owns it as a product, not alongside another book of work. That is this role.
Founding platform engineer. Owner, not operator.
This is a founding platform hire. You are the founding engineer devoted solely to Trust.ML — you build the product, you set the bar, and you shape the small team that follows. The platform lives in the open between our advisory engagements and our SaaS customers. The two feed each other. Patterns proven in bespoke client work become productized capabilities. Customer feedback from the platform sharpens the advisory practice.
You own the roadmap, the release cadence, and the on-call posture. You shape the engineering hires that follow you, alongside the founder and original designers. Founder oversight on commercial strategy; full latitude on how the platform is built and shipped. If you have wanted to build a product from a clean slate without having to apologize for the rest of the company, this is that seat.
Platform, team, and cadence.
- Own the Trust.ML roadmap end-to-end. Translate patterns from advisory engagements into productized capabilities our SaaS customers can adopt without our hands on the keyboard.
- Start as the team. Day one you are the engineer on Trust.ML — writing the code, owning the architecture, talking to the first customers. As the platform grows, you help hire and mentor the engineers who join you.
- Set the AI tooling posture. Claude Code, Cursor, agentic dev loops as default. You use what you preach, and so will every engineer you hire after you. We ship 25+ PRs a day; you should exceed that, and then some.
- Establish the release cadence. Weekly if not daily platform releases, monthly customer-facing milestones, quarterly architecture review. Discipline required for something thousands and, we hope millions, will rely upon.
- Operate the platform. SLOs, on-call rotation, incident review. The kind of operational hygiene that lets a CEO trust the tool with their workflow.
- Partner with the advisory practice. Sit in on engagement reviews. Identify what should graduate from bespoke build to platform feature, and what should not.
- Own customer engineering. Onboarding, integration support, the technical side of renewals. You are the engineering voice the customer hears.
- Shape the governance surface. Approved-tool catalog, audit logging, eval coverage, escalation pathways. The product expression of the firm's posture.
The shape of the right person.
Must-haves
- Five plus years building software, including time as the engineer-in-charge of a platform or product that real customers depended on.
- AI-pilled and full-stack. You reach for agents instinctively and have opinions about which ones. But you got fluent with code first — Cursor multiplies you; it did not teach you.
- You have taken a platform from zero-to-one or one-to-ten. You know the difference between a feature factory and a platform, and you know which one this is.
- Production experience with LLM applications. Not just demos. You have shipped retrieval, evaluation, and tool-using systems that customers depended on.
- Operational chops. SLOs, on-call, incident review, audit logging. You have done the unglamorous work that makes a platform trustworthy.
- You hire well and give honest feedback to engineers who need it. Reference-checked on both.
- Clear written communication. The platform's first customers are CEOs of mid-market firms. They will read your release notes.
Nice-to-haves
- Founder or founding-engineer experience at a seed-to-Series-B SaaS company. Not required, but the shape of a person who has built something from nothing fits this seat.
- You have run a small platform engineering team at a SaaS company or the platform group inside a larger product org.
- You have worked at the intersection of advisory and product in some form. Consulting-to-platform, agency-to-product, or research-to-product transitions are all relevant.
- You have built governance, eval, or trust surfaces for AI products. Not the marketing version. The actual one.
- Cloud infrastructure (AWS, Azure, GCP) at production scale, with a security and compliance posture you are willing to defend.
- You have an opinion about what enterprise AI should feel like in five years that is not just a longer version of what it looks like today.
To the founders.
Reports directly to the founding partners on commercial strategy and platform direction. You own the build, it is not our job to second-guess it.
Calibrated to the seat.
Compensation is competitive with senior platform roles at venture-funded AI infrastructure companies, with equity reflecting the founding nature of the seat.
Three things, by email.
Send to talent@catalyst.wf with "Platform Manager - Trust.ML" in the subject line:
- A short cover letter. Who you are, what you have run, why Trust.ML.
- A platform or product you have run, with a paragraph on what you owned, what you changed, and what you would do differently.
- An opinion on Trust.ML, after spending an hour on what we have published. Where would you take it. What you would cut.