> Markdown version of [/jobs/ext/3854437-software-engineer-ml-platform](https://www.wearedevelopers.com/jobs/ext/3854437-software-engineer-ml-platform). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Software Engineer, ML Platform

- **Company:** Bewirb Dich Jetzt!
- **Location:** Wien, Austria
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Clinical Data Repository, Cloud Computing, Information Engineering, Data Governance, Data Infrastructure, Distributed Computing Environment, Distributed Systems, Job Scheduling, Python (Programming Language), Machine Learning, Open Source Technology, Azure Machine Learning, Software Engineering, Management of Software Versions, Data Processing, Pytorch, Model Validation, AI Coding Agents, Backend, Machine Learning Operations
- **Published:** October 22, 2026
- **Apply:** https://www.studentjob.at/stellenangebote/8785437-software-engineer-ml-platform

## About the Role

* Strong software engineering: Excellent Python skills and experience building maintainable production systems.
 * ML systems experience: Familiarity with PyTorch training, model evaluation, GPU workloads, and the ML development lifecycle.
 * Distributed systems: Experience with cloud infrastructure, containers, orchestration, job scheduling, or distributed computing.
 * Data engineering: Experience building reliable pipelines and working with large, versioned datasets.
 * Operational mindset: You care about observability, debuggability, failure recovery, and clear system boundaries.
 * Platform thinking: You build reusable capabilities instead of solving the same problem repeatedly.
 * Research empathy: You understand that research workflows change quickly and infrastructure must support exploration.
 * AI-native workflow: You use coding agents, automation, and custom tooling to increase your own leverage and that of the team.

## Description

As a Software Engineer on the ML Platform, you will build the systems behind every nyra labs experiment and model release.

You will work across data processing, distributed training, experiment management, evaluation, inference, and release infrastructure. Your goal is to give a small research team the leverage to run ambitious experiments quickly, reproducibly, and reliably.

This is not a conventional backend role. You will work directly with researchers, understand how models are developed, and turn recurring research bottlenecks into dependable platform capabilities.

Why we need you

Strong research depends on more than strong ideas.

Training data must be versioned and traceable. Experiments need to be reproducible. Evaluations must run consistently. Models need to move from a researcher's environment into efficient inference and public releases without fragile manual steps.

The sensitivity and scale of clinical speech data add another challenge: the platform must enable fast research while maintaining strict standards for security, privacy, and data governance.

We need an engineer who sees infrastructure as a force multiplier for research., * Data platform: Build reliable pipelines for ingesting, validating, transforming, versioning, and accessing large speech datasets.

 * Training infrastructure: Improve distributed training, orchestration, checkpointing, resource scheduling, and failure recovery.
 * Experiment systems: Create tooling for configuration, tracking, comparison, reproducibility, and artifact management.
 * Evaluation platform: Make it easy to run benchmarks, inspect regressions, compare releases, and understand model behavior.
 * Inference: Optimize models for efficient cloud and on-device use where relevant.
 * Release infrastructure: Automate model packaging, documentation, validation, and open-source publishing.
 * Developer experience: Build internal tools that remove friction from the daily work of researchers and engineers.
 * Reliability and security: Establish observability, access controls, and operational practices appropriate for sensitive clinical data.