AI Systems Engineer AI Systems Engineer

Embreier builds Kalyr, the consequence record for enterprise AI. For every recurring decision in an operation, human or AI, Kalyr seals the forecast before action, records what was permitted and done, and settles the outcome after it in the company’s own economics. Platforms run the work;
Kalyr proves what it earned, and learns from it. We are building intelligence that learns from reality wherever consequential work is done.

You own the runtime that makes the whole loop work, end to end, inside real enterprise environments:

Kalyr sits around the models. They are rented and scored; the scarce part is the system that holds every one of them to account. You decide what to build and, as often, what to leave out. The questions are concrete: “What did the system know at 09:14 on the day of that decision?” or “Can we replay last quarter exactly, a year from now, and get the same answer?”

• Hold time exactly. Event and bitemporal pipelines that answer what was known, and when, for every decision.
• Seal and sign. Forecasts committed before outcomes exist, in an append-only, hash-chained audit trail anyone can verify.
• Score any model. Adapters that let a person, a rule, a model or an agent be judged on the same evidence, and swapped without losing history.
• Replay, then shadow. Deterministic replay over a customer’s own history, then prospective runs beside the method already installed.
• Connect the enterprise. Read-only connectors into MES, historians and ERP, with a purpose and a permission on every read.
• Ship inside the customer. Tenant isolation, rights, erasure and reproducible deployment in customer environments.

• Systems that shipped. Backend and data systems you owned in production, in fluent, typed and tested Python, and the scars to show for it.
• Data in time. Event streams, temporal models, lineage and PostgreSQL done properly.
• Real ML fluency. Evaluation, calibration, leakage, chronology and baselines. You build the infrastructure around models and know exactly how it can lie.
• First principles. When something is unclear, you design the test before you form the opinion.
• Read, then redesign. You understand an unfamiliar codebase before you improve it.

Python 3.12, FastAPI, Pydantic v2, PostgreSQL, SQL Alchemy, Alembic and pytest; infrastructure as code, containerised deployment and cryptographic signing. We rent frontier models and commodity infrastructure, and own the middle.

Strong additions: industrial data (OPC UA, historians, MES, ERP), causal inference or sequential decision-making, ML platforms and inference, enterprise security.

• You want to build what has never existed, and you have the depth to build it correctly.
• Long, intense stretches of building give you energy, and you stay precise and grounded deep into them.
• You would rather own a hard system end to end than a slice of an easy one.
• You write the code yourself, every day, and hold it to the standard you would expect from anyone else.

Original thinkers. People who learn a rule well enough to follow it, then bend it to build something better. You contribute in work others can build on, and you measure yourself by it. You are glad to be known by what you create and by what you give. Proud of the work, humble in the room. The shared result comes first.

Accountability here means being the first to say the true thing that needs saying, for the good of the mission we share.

Embreier is a Swiss company based in Lausanne. Engineering, research and people who know operations from the inside build in one team, across languages and disciplines. High standards and appreciation live side by side here: the work is demanding, and the people doing it are seen.