Software Engineer, ML Platform

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.

Please note: While we are currently accepting applications for this role, we are not actively hiring for it at the moment. We’ll review relevant profiles and reach out should a suitable opportunity arise.

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.