AI role · Open offer

Senior PyTorch Engineer — research & production models

Employer verified

LatticeMind AI

Full-time
Senior
PyTorch & ML research
Hybrid
Torstrasse 164, Berlin, BE 10115, Germany
Berlin, Germany
€95k–€125k + equity

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

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At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: Torstrasse 164, Berlin, BE 10115, Germany
Compensation: €95k–€125k + equity
Experience level: Senior
Team: PyTorch & ML research
Benefits & perks

About this role

What the team is looking for and how this position fits.

LatticeMind AI is hiring a Senior Senior PyTorch Engineer — research & production models on the PyTorch & ML research team (Hybrid · Berlin, Germany). This is a hands-on role in search & recommendations—not a generic “AI enthusiast” post.

You will own ranking, retrieval, and online experimentation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

LatticeMind AI — Own the PyTorch stack from exploratory notebooks to stable training pipelines and on-device exports. You will partner with scientists on architecture search, implement reproducible experiment tracking, and harden checkpoints for mobile and edge inference.

Berlin hybrid 2 days / week; EU remote considered for exceptional PyTorch depth.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
Docker

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
AWSTool
Regular use of AWS in the search & recommendations stack—configuration, debugging, and team conventions.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Link a public or redacted repo where PyTorch is central (training loop, custom autograd, or export path).
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, Docker, CUDA, and Kubernetes.
You have owned ranking, retrieval, and online experimentation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Berlin, Germany)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.

Apply

Candidate accounts only: add your headline and bio on , and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

Link a public or redacted repo where PyTorch is central (training loop, custom autograd, or export path).

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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