ML Product Engineer

Job description

At kausable, 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 PyTorch. 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 kausable ships ML: the patterns, tooling and standards between research and production. As t…