Get hired atJetty×The Anti Job Board

How to Actually Get Hired

Who reads applications, which channel gets a reply, and what they screen for.

<50 peopleMontreal, CanadaAI

Don't make these mistakes

Enthusiasm about AI agents generally. Jonathan has been building and researching AI accountability for years. He will hear "I'm excited about the agentic AI space" as noise. Lead with the specific verification failure you have witnessed or the specific technical work you have done. The product thesis is precise. Your outreach should be too.

What gets their attention

AQC Capital and Hidden Layers Capital co-leading a pre-seed for a founder with a McGill/Mila PhD, Meta AI credits, and a published thesis on privacy-preserving ML is a research-credibility bet. They are backing the specific intellectual background that produced the runbook thesis, and they expect Jonathan to hire people with equivalent rigour. Hidden Layers Capital's name is itself a signal: neural network hidden layers are where the non-interpretable computation happens. A fund named after that concept is specifically betting on the infrastructure that makes AI systems more interpretable and accountable. The Akinox strategic investment is the most interesting signal in the cap table. Akinox builds digital health coordination platforms for hospital networks across Quebec and Canada: patient flow, care coordination, and inter-hospital communication infrastructure for the public health system. Their check is not a financial bet. It is a customer signal. Akinox is deploying AI in environments where error has regulatory and patient safety consequences. Jetty's runbook and evaluation infrastructure is exactly what a health IT platform needs before it can put AI agents into hospital workflows. Healthcare and government are the first and most urgent enterprise verticals. MLCommons is the second strategic signal. MLCommons is the industry consortium that creates standardized benchmarks for AI systems: its members include Google, Meta, Intel, Nvidia, Microsoft, and most major AI research organizations. Its participation in Jetty's round means Jetty is being positioned not just as a product but as governance infrastructure. MLCommons cares about standards. Jetty's open-source tools for model provenance and AI governance are designed to connect to those standards. The stated use of capital is explicit: accelerate product development, expand engineering, support enterprise traction. Three parallel mandates. Apply: apply@jetty.io

Why applying the normal way doesn't work

At <50 people, Jetty has no recruiting team. Your application lands with a founder who is also running sales, product and payroll. The obstacle isn't a queue or an ATS, it's being seen at all. Cold outreach outperforms the form here, consistently.

Who to contact at Jetty

Who decides:a founder or department head
Best channel:LinkedIn or direct email

Founding team

JL
Jonathan Lebensold
Founder & CEO

Jonathan Lebensold's career moves in a specific pattern: ship something, then go understand why it breaks. He co-founded Paradem, a Montreal software consultancy that built line-of-business applications for startups and enterprises, and during that period wrote the React Native Cookbook for O'Reilly, one of the earliest comprehensive guides to cross-platform mobile development. Technical book writing at that level requires explaining a system end-to-end to someone who has never seen it with enough clarity to survive scale. He then pivoted hard into research: completing a PhD at McGill University and Mila under Borja Balle (now at Google DeepMind, one of the leading differential privacy researchers in the world) and Doina Precup (one of the most cited reinforcement learning researchers globally, co-director of Mila's Montreal office). His doctoral work focused on differential privacy, privacy-preserving machine learning, and generative model accountability: the formal discipline of proving that an ML system did what you permitted it to do, not just what you wanted it to do. He then worked as a visiting researcher at Meta AI on privacy-preserving ML and at Reliant AI on AI reasoning before founding Jetty. His Google Scholar profile has 1,331 citations. He is also a contributor to OpenMined's PySyft, implementing websocket infrastructure for distributed privacy-preserving ML, which is how he entered the research community before the formal PhD. His Substack, Ground Truth, is the most substantive window into his thinking: the post "Generation Got Cheap. Verification Didn't." is a 2,500-word rigorous case, citing an MIT paper on the Measurability Gap, that constructs the intellectual argument for why Jetty exists. He appeared on Nick Taylor's Streams to discuss agent hill-climbing and evaluation. His X bio reads: "AI has an evaluation problem and I'm trying to fix it." That is the product thesis in nine words. He books his own demos via Calendly. His family maintains a shared website at lebensold.ca where his personal page sits alongside those of Esther, Julian, and Suzanne. An early consultancy bio notes he enjoys baking apple pie. His academic supervisors are two of the most rigorous researchers Mila has produced. Both details are real and neither is a contradiction.

EH
Ezra Hopkins
Co-founder

Ezra Hopkins is the UX and design co-founder at Jetty, and his path to that role runs through an unusually broad set of contexts. Earlier in his career he was a project lead at the Baha'i organization, contributing to initiatives around global unity, the harmony of science and religion, and international cooperation. That is not a common background for a software product designer, and it is not irrelevant: working in a governance-oriented, values-driven international organization builds intuition about how people make decisions under uncertainty and how information needs to be communicated to people who are not technical specialists. He then moved into design and web development professionally, working at TimeZoneOne, an international creative communications agency, designing web interfaces and applications using a ColdFusion-based CMS, and at Terabyte Interactive, building web pages with a .NET-based CMS while managing client relationships and subcontractor coordination. His design work is grounded in enterprise software interfaces: complex, information-dense, built for operators who need clarity under pressure, not for consumers who need delight. He joined Jonathan at Paradem as UX Lead, where the same brief applied: line-of-business applications that had to work reliably for people who could not afford to misread an interface. At Jetty, that problem is sharpened. The trace viewer, the escalation notice, the runbook editor, these are products that non-technical hospital administrators or financial operations leads will use to determine whether to trust an agent's output. If those surfaces are confusing, the verification problem does not get solved. Ezra is the co-founder responsible for making sure they are not confusing. His public profile is concentrated on LinkedIn and design portfolio work rather than social media or writing.

What to show them

Jetty's core technical product is the evaluation loop: an agent that runs, checks its own work against defined criteria, and retries or escalates when it fails. Building that reliably at scale, across multiple models and agent frameworks, while maintaining the tracing layer that makes every decision auditable, is the central engineering problem. Jonathan has a research background in privacy-preserving ML and AI safety; the engineer he hires for this role will be someone who thinks about agent behaviour with the same rigour. Core skills: Python, LLM agent evaluation frameworks, sandboxed execution environments (Docker, containers, isolated runtimes), distributed tracing systems (OpenTelemetry or custom), agent framework internals (LangGraph, AutoGen, or custom), CI/CD integration, GitHub Actions. Proof of work: Build a minimal evaluation harness for a two-step agent workflow: the agent executes a task, runs a self-check against explicitly defined success criteria, and either passes or retries with an explanation. Publish the code. Write a README explaining your choices for the success criteria schema and the retry logic design. The design of the criteria schema is the proof of engineering thinking here.

A cold email that works at Jetty

Subject: Founding Engineer (Agent Infrastructure / Evaluation Systems), [your one-line proof]
Hi Jonathan, I built a minimal self-evaluating agent harness where the success criteria are defined as a schema separate from the task instructions: [link]. The interesting design decision was how the agent formats its self-check so it is both machine-readable for logging and human-readable for escalation. I have been following your writing on the verification gap and think this pattern maps directly to what Jetty needs at the core. Worth 20 minutes?

What Jetty screens for

Screening signal: AQC Capital and Hidden Layers Capital co-leading a pre-seed for a founder with a McGill/Mila PhD, Meta AI credits, and a published thesis on privacy-preserving ML is a research-credibility bet. They are backing the specific intellectual background that produced the runbook thesis, and they expect Jonathan to hire people with equivalent rigour. Hidden Layers Capital's name is itself a signal: neural network hidden layers are where the non-interpretable computation happens. A fund named after that concept is specifically betting on the infrastructure that makes AI systems more interpretable and accountable. The Akinox strategic investment is the most interesting signal in the cap table. Akinox builds digital health coordination platforms for hospital networks across Quebec and Canada: patient flow, care coordination, and inter-hospital communication infrastructure for the public health system. Their check is not a financial bet. It is a customer signal. Akinox is deploying AI in environments where error has regulatory and patient safety consequences. Jetty's runbook and evaluation infrastructure is exactly what a health IT platform needs before it can put AI agents into hospital workflows. Healthcare and government are the first and most urgent enterprise verticals. MLCommons is the second strategic signal. MLCommons is the industry consortium that creates standardized benchmarks for AI systems: its members include Google, Meta, Intel, Nvidia, Microsoft, and most major AI research organizations. Its participation in Jetty's round means Jetty is being positioned not just as a product but as governance infrastructure. MLCommons cares about standards. Jetty's open-source tools for model provenance and AI governance are designed to connect to those standards. The stated use of capital is explicit: accelerate product development, expand engineering, support enterprise traction. Three parallel mandates. Apply: apply@jetty.io

Customize your CV for the Jetty role. Matching the job description language helps clear ATS filters.

Mistakes that kill applications

Generic CVs stand out at a <50-person company — and not in a good way. Fastest path to rejection.

Skip 'I'm looking for...' — start with Jonathan's framing for the market timing is precise and worth understanding and your specific angle on solving it.

Applying and waiting = silence. Follow up at day five — it roughly doubles your odds of a reply.

Frequently asked questions

How do I apply to Jetty?

Through the roles on our Jetty jobs page, or directly to a founder or department head if you can reach them. At <50 people, direct outreach outperforms the form.

Does Jetty respond to cold emails?

Response rate data for Jetty not yet confirmed.

Who is the hiring manager at Jetty?

At this size, hiring is usually run by a founder or department head.

How competitive is it to get hired at Jetty?

Expect 50-100 applicants in the first two weeks for AI roles at this size. Apply within 72 hours for best odds.

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