ML Product Engineer

kausable GmbH; Heidelberg; On-site; Vertraulich; Full time

Benefits offered by this job

VSOP equity

30 days holiday per year

Statutory social insurance

Conference travel

Flexible hybrid work

High-end laptop

Job summary

kausable is seeking an ML Product Engineer to bridge research and production, owning the path from promising lab results to dependable production capabilities. You will handle serving, evaluation, data pipelines, reliability, latency and cost, while aligning with researchers and product stakeholders.

You will ship ML-powered services, design scalable pipelines, and own model releases with a pragmatic, outcome-driven mindset to drive real user impact.

Qualifications

• Shipping ML-powered systems to production and operating them after launch.
• Strong software engineering in Python with PyTorch experience.
• Experience with model serving, APIs, containers and cloud infra.
• Judgment on evaluation, observability, reliability and production trade-offs.
• Ability to work with customers, researchers and product stakeholders.
• Outcome-oriented mindset focused on dependable capabilities.

Responsibilities

• Turn research models into production-grade services with reliability, latency and cost targets.
• Build evaluation harnesses and release criteria to quantify readiness to ship.
• Design data pipelines, versioning and observability across training, evaluation and live inference.
• Build stable APIs and developer-facing abstractions around models.
• Collaborate with researchers to expose failure modes and improve models and evaluations.
• Translate customer needs into reusable platform capabilities.
• Own model releases, monitoring and rollback patterns as production scales.

Skills

Python

PyTorch

Model serving

APIs

Containers

Cloud infra

Observability

Customer interaction

Senior-level

Tools

Docker

AWS

RunPod

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 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.

Our Culture

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

• 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 PyTorch.
• Weights & Biases and model-evaluation tooling.
• Docker, AWS, RunPod and comparable cloud infrastructure.