THE PROJECT

Neurode Labs is building brain-computer interface technology that generates continuous, high-fidelity neural signal data from devices in the field. Making that data useful — reliably, at scale, in a form that actually trains great models — is the job.

We're hiring a Data & ML DevOps Engineer to build our data infrastructure from the ground up. You'll work alongside our ML engineer, backend engineer, and data security engineer — but this function is yours. Greenfield, high ownership, no inherited mess to untangle.

WHAT YOU'LL BE BUILDING

In your first six months, you'll design and build the data infrastructure that sits beneath our ML stack — taking raw neural signal data from devices in the field and making it pipeline‑ready for model training, at the petabyte scale.

- Design and build scalable ingestion and processing pipelines for high-volume, high-availability data
- Architect distributed computing systems that hold up under load
- Ensure data quality, integrity, and provenance across the full pipeline
- Work closely with the backend engineer to handle data flowing in from a large fleet of concurrently connected edge devices
- Build and maintain infrastructure for training deep learning models at scale
- Set up and optimise MLOps tooling: experiment tracking, model versioning, deployment pipelines
- Collaborate with the ML engineer to make sure data reaches models in the right shape, at the right time

Platform & ops

- Own cloud infrastructure on AWS — provisioning, cost management, reliability
- Instrument systems for observability: monitoring, alerting, debugging at scale
- Build with security and compliance requirements in mind from day one

WHAT WE'RE LOOKING FOR





- 3+ years of hands‑on experience in data engineering or ML infrastructure — in production, not just in notebooks
- Demonstrated experience working with terabyte‑plus‑scale datasets
- Solid grasp of distributed computing: you've built systems that scale horizontally and you understand why they fail
- Solid understanding of ML model training fundamentals — you don't need to train the models, but you need to know what they need
- AWS fluency: you can architect, deploy, and manage cloud infrastructure without hand‑holding
- High availability mindset: you build for uptime, not just throughput

NICE TO HAVE

- Experience with time‑series or streaming data (particularly relevant for continuous neural signal ingestion)
- Familiarity with MLOps platforms — Databricks, MLflow, similar
- Background at a data‑heavy company: frontier AI labs, robotics, quantitative finance, or large‑scale consumer platforms

WHO YOU ARE

- A systems thinker — you see the whole architecture before you write the first line
- Opinionated and direct: you have a point of view on how things should be built, and you'll push back if something doesn't make sense
- Analytically rigorous but pragmatic — you know the difference between elegant and over‑engineered
- A strong communicator of complex ideas — you can explain a pipeline design to an ML engineer and a data security engineer in the same conversation without losing either of them
- Comfortable with ambiguity — greenfield means no playbook, and that energises you rather than slowing you down
- You're based in Sydney and want to work in person with a small, technically ambitious team

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📌 Data/ML DevOps Engineer (City of Sydney)
🏢 Neurode
📍 City of Sydney