Software Engineer, ML Platform

Crane Venture Partners; Wien; Hybrid; EUR 90.000 - 130.000; Vollzeit

Benefits dieser Stelle

Phantom Stock Options

Company benefits

Vienna office in First District

Zusammenfassung

nyra health is building software that supports clinics, therapists, and patients in neurorehabilitation. nyra labs transfers challenging problems into open models and data for the wider community.

nyra health’s ML Platform team enables researchers to run ambitious experiments quickly, reproducibly, and reliably. In Vienna, you will join a hybrid team, collaborating with researchers to turn bottlenecks into durable platform capabilities.

Qualifikationen

• : Strong software engineering skills in production systems.
• : Proficiency with PyTorch and ML lifecycle.
• : Experience with cloud infrastructure and distributed computing.
• : Building reliable data pipelines and versioned datasets.
• : Focus on observability, debuggability, and fault recovery.
• : Ability to build reusable platform capabilities.
• : Understanding research workflows and infrastructure needs.
• : Use of automation and tooling to boost team leverage.

Aufgaben

• : Build reliable pipelines for ingesting, validating, transforming, versioning, and accessing large speech datasets.
• : Improve training infrastructure, orchestration, checkpointing, and resource scheduling.
• : Create tooling for configuration, tracking, and reproducibility of experiments.
• : Develop an evaluation platform to run benchmarks and analyze model behavior.
• : Optimize models for cloud and on-device inference where relevant.
• : Automate model packaging, documentation, validation, and publishing.
• : Improve developer experience by removing friction for researchers and engineers.
• : Establish observability, access controls, and security practices for sensitive data.

Kenntnisse

Python

ML systems

Distributed systems

Data engineering

Observability

Platform thinking

Research empathy

AI-native workflow

Tools

PyTorch

Docker

Kubernetes

Jobbeschreibung

Job description

About the role

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.

About the company

At nyra health, we build software that supports clinics, therapists, and patients throughout neurorehabilitation. myReha delivers personalized therapy, while nyra insights helps clinical teams manage and understand patient progress.

nyra labs is the research arm of nyra health. We turn difficult problems encountered in practice into open models, datasets, benchmarks, and research that the wider community can build on.

If that resonates with you, we would love to hear from you.

What you’ll shape

• 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.

Job requirements

What sets you up for success

• 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.

Beyond your CV

• Pragmatic: You know what needs a platform and what needs a small script.

• Leverage-orientated: You look for improvements that make the entire team faster.

• Reliable: You treat reproducibility and data integrity as core product requirements.

• Self-directed: You can identify bottlenecks and own the solution end to end.

• Collaborative: You enjoy working closely with researchers and translating experimental needs into durable systems.

Why nyra labs

• Build the infrastructure behind open speech models and research

• Work directly with researchers instead of operating as a separate platform team

• Solve difficult systems problems involving large-scale audio and sensitive data

• High ownership with visible impact on every experiment and release

• Direct collaboration with founders and the wider nyra health engineering team

• Attractive compensation, Phantom Stock Options, and company benefits

• A beautiful office in Vienna’s First District with a hybrid working model

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