(Computational) Machine Learning Engineer

Who We Are

Nomad Atomics is on a mission to make the broad uptake of quantum sensing a reality and simultaneously push the limits of our field beyond what we think is possible. We are building the world's most advanced fit‑for‑purpose quantum sensors to allow us to see the world like never before.

Who You Are

You are a voracious learner, a problem solver, and a doer. You are fascinated by emerging technologies and excited to help build a company with ground‑breaking ideas. You are excited to operate at the forefront of technology development and be the first in the world to demonstrate the true capability of these world‑leading sensors. You have an innate attention to detail and enjoy the challenge of modelling real‑world systems. If you love a challenge and use your skills and creativity to solve anything that comes your way, you are a fit for this role.

Your Role

If you love building immaculate computational software in Python and have good mathematical acumen, this role may be for you. Working hand in hand with the Nomad ML & Analytics Team and the Geophysics Team, you will be the driving force that translates cutting‑edge research and complex algorithms into a robust, scalable, and production‑ready analytics software. This is an exceptionally hands‑on role for a skilled computational machine learning engineer who is passionate about building software that solves fundamental scientific challenges.

Responsibilities

- Collaborate with our ML & Analytics Team and geophysical subject matter experts to build, test, and maintain computational software and quantum gravity sensor data processing pipelines.

- Take novel computational techniques and algorithm prototypes designed by our research team and engineer them into reliable, performant software modules.

- Build robust data ETL pipelines and software scaffolding.

- Develop comprehensive unit and integration tests to ensure the scientific accuracy and reliability of our codebase.

- Contribute to our DevOps and MLOps practices, including containerisation (Docker), CI/CD pipelines, and future deployments on cloud platforms (AWS).

- Work with our technology and deployment experts to build the software tools needed for highly efficient surveying techniques.

Requirements

- Exceptional machine learning/computational software development skills in Python (required).

- A strong, demonstrated background in DevOps and MLOps, including version control (Git), CI/CD, API design, unit testing, data and experiment tracking, and object‑oriented programming (required).

- A strong quantitative intuition and the proven ability to translate complex mathematical concepts from domains like machine learning, signal processing, and statistical simulation into high‑quality, efficient code (required).

- Demonstrated experience with Python libraries: Scipy, Numpy, JAX, Pytorch, Tensorflow, Matplotlib, Plotly, PyMC, multiprocessing.

- 3+ years of professional experience in a computational software development or data‑intensive role, OR a portfolio of personal projects that demonstrates an equivalent level of skill and a passion for building complex scientific software.

- A degree in a quantitative field such as Computer Science, Engineering, Physics, or Mathematics.

- Experience applying machine learning techniques to solve real‑world scientific or engineering problems.

- A demonstrated ability to effectively communicate complex ideas and problem‑solve within fast‑paced team environments.

- A history of thriving in diverse environments that value honesty, open communications, and strong bonds between team members.

- You must be an Australian Citizen or a Permanent Resident.

Nice‑to‑Haves

- Degree in Geophysics or experience in geophysical modelling/inversion techniques.

- A Master’s or a PhD in quantitative methods.

- Familiarity with Bayesian statistics.

- Familiarity with Docker, AWS, and Linux.

Benefits

The role is full‑time and based in Melbourne, Australia. We have the flexibility to work from home from time to time, but the in‑person interaction with our tech and geophysical teams will be critical. We offer a competitive salary, employee share option package, and opportunities for professional growth and advancement.

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