ML Product Engineer

At the company, we build causal, reasoning-first models that learn from a handful of examples and generalize across domains.

Research gets us to a capable model.

This role gets that model into the hands of users.

As our ML Product Engineer, you own the path from a promising result in the lab to a dependable production capability: serving, evaluation, data flows, reliability, latency and cost.

You will work at the boundary between research and product, where good technical judgment matters more than a clean handover.

• Tasks
• Turn research models into production-grade services with clear reliability, latency and cost targets.
• Build evaluation harnesses and release criteria that show quantitatively when a model is ready to ship.
• Design the data pipelines, versioning and observability needed across training, evaluation and live inference.
• Build stable APIs and developer-facing abstractions around our models.
• Work closely with researchers to expose failure modes and turn product feedback into better models and evaluations.
• Translate customer and design-partner needs into reusable platform capabilities rather than one-off solutions.
• Own model releases, monitoring and rollback patterns as the production footprint grows.

Requirements

• A track record of shipping ML-powered systems to production and operating them after launch.
• Strong software engineering skills in Python and hands-on fluency with Py Torch.
• Experience with model serving, APIs, containers and cloud infrastructure.
• Sound judgment around evaluation, observability, reliability and production trade-offs.
• The ability to work directly with customers, researchers and product stakeholders.
• A pragmatic, outcome-oriented mindset: you optimize for dependable capabilities that users can actually adopt.
• We are primarily hiring at senior level. We are also open to exceptional candidates with fewer years of experience who can demonstrate comparable depth, judgment and ownership.

Nice to have

• In-context learning, PFNs, synthetic data or probabilistic models.
• Weights & Biases, model registries, CI for models or comparable MLOps tooling.
• SDK or developer-tooling design.
• Security, privacy or on-premise deployment requirements.
• Prior startup, design-partner or 0-to-1 product experience.

Benefits Where This Can Go

You will define how the company ships ML: the patterns, tooling and standards between research and production.

As the team grows, the role can expand into technical ownership of the model-to-product stack or leadership of a small ML product group.

The trade‑off is part of the job: shipping quickly matters, but only when the resulting system remains measurable, reusable and dependable.

We are "Putting Science at the Core of AI". That means we

• Our Culture
• are scientists at heart, with a builder's mindset,
• are open to challenge, grounded in curiosity and respect,
• welcome diverse perspectives and value thoughtful, open debate,
• focus on outcomes and real‑world impact,
• foster an environment of support, inspiration, and freedom for everyone to do their best work.

Perks & Benefits

• VSOP equity: a real stake in what we build.
• 30 days of paid holiday per year.
• Statutory social insurance.
• Conference travel and role‑relevant learning.
• Flexible hybrid work, with roughly one in‑person team meet‑up per month.
• A high‑end laptop and access to the cloud compute required for the role.
• Tools and Infrastructure
• Python and Py Torch.
• Weights & Biases and model-evaluation tooling.
• Docker, AWS, Run Pod and comparable cloud infrastructure.
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