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 outstanding 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
Strong 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
#J-*****-Ljbffr

📌 Data/Ml Devops Engineer (New South Wales)
🏢 Neurode
📍 New South Wales