Job Description

THE PROJECT

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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.

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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.

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WHAT YOU'LL BE BUILDING

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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.

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

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

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

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

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- A systems thinker — you see the whole architecture before you write the first line
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- 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
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- Analytically rigorous but pragmatic — you know the difference between elegant and over‐engineered
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- 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
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- Comfortable with ambiguity — greenfield means no playbook, and that energises you rather than slowing you down
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- You're based in Sydney and want to work in person with a small, technically ambitious team
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#J-18808-Ljbffr

📌 Data/ML DevOps Engineer (New South Wales)
🏢 Neurode
📍 New South Wales