ML Product Engineer

United States Digital Space LLC; Heidelberg; Vor Ort; EUR 120.000 - 160.000; Vollzeit

Benefits dieser Stelle

VSOP equity

30 days paid holiday

Statutory social insurance

Conference travel and learning

Flexible hybrid work

High-end laptop

Zusammenfassung

United States Digital Space LLC is seeking an ML Product Engineer to bridge research and production. You will turn promising lab results into reliable services, define evaluation criteria and manage data pipelines, serving, latency and cost.

You will collaborate with researchers and customers, design stable APIs, and own model releases as the production footprint grows. The role emphasizes practical, measurable, and reusable ML capabilities with a focus on end-user impact.

Qualifikationen

• Track record of shipping ML-powered systems to production.
• Strong Python and PyTorch skills.
• Experience with model serving, APIs, containers and cloud infra.
• Good judgment on evaluation, observability, reliability and trade-offs.
• Ability to work with customers, researchers and product stakeholders.
• Outcome-oriented mindset focused on dependable capabilities.
• Senior level focus; exceptional candidates with fewer years considered.

Aufgaben

• 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.

Kenntnisse

Python

PyTorch

APIs

MLOps

Cloud infra

Observability

Customer collaboration

Tools

Docker

AWS

RunPod

Weights & Biases

Jobbeschreibung

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