Algorithm Engineer

ProFound People; City of Melbourne; Hybrid; AUD 90,000 - 130,000; Full time

Job summary

ProFound People in Melbourne is expanding its engineering function and seeks an Algorithm Engineer to modernise analytical models for our sensor data.

This full‑time hybrid role owns detection and location algorithms, works with historical datasets and collaborates with software to validate and deploy models into production.

Ideal candidates hold a quantitative degree, strong statistical background, and proficiency in Python and MATLAB.

Qualifications

• Strong foundation in statistical modelling and mathematical problem solving, applied to real world, noisy datasets.
• Proficiency in Python and MATLAB for data analysis and algorithm development.
• Experience working on complex, technically demanding analytical problems, ideally involving sensor data.
• Strong communication skills and ability to collaborate with software and engineering teams.

Responsibilities

• Review and rebuild analytical algorithms for detection, location and prioritisation from sensor data.
• Work with historical datasets to identify where models fall short and design improvements.
• Apply statistical and mathematical methods to extract meaningful patterns from large datasets.
• Collaborate with the software engineering team to validate models and support deployment into production.
• Help establish the working practices and documentation of a new data analytics function.
• Communicate findings and model performance to both technical and non-technical stakeholders.

Skills

Statistical modelling

Data analysis

Mathematical problem solving

Communication skills

Education

Degree in mathematics, statistics, physics, engineering or related field

Tools

Python

MATLAB

Job description

An established IoT technology company operating across four continents is expanding its engineering function and is now hiring an Algorithm Engineer to help modernise the analytical models at the core of its product.

In this role you will take ownership of a set of detection and location algorithms, bringing fresh analysis and updated statistical and mathematical thinking to improve how the business identifies and prioritises findings from its sensor network. You will work entirely with data that has already been captured and transformed, using historical records alongside newer engineering knowledge to rebuild and refine these models, rather than working on embedded or edge level signal processing. This is a foundational hire for a newly forming data analytics function, working closely alongside the software team to validate, test and eventually support deployment of updated algorithms into production. It suits someone who enjoys getting into the detail of a real world dataset, applying strong statistical and mathematical reasoning rather than off the shelf machine learning, and who is comfortable being one of the first people to establish a new capability inside an engineering organisation. The ideal background is one shaped by technically complex or scientifically grounded problems, rather than more straightforward commercial or transactional analytics.

This is a full time, Melbourne based role working in a hybrid model with an expectation of three days per week in the office. For interstate candidates, relocation is required.

Key Responsibilities:

• Review and rebuild existing analytical algorithms used to detect, locate and prioritise findings from field deployed sensor data.
• Work with historical and current datasets to identify where existing models fall short and design improved versions.
• Apply strong statistical and mathematical methods to extract meaningful patterns and attributes from large datasets.
• Collaborate closely with the software engineering team to validate models and support their eventual deployment into a production environment.
• Help establish the working practices, documentation and technical foundations of a newly forming data analytics team.
• Communicate findings and model performance clearly to both technical and non-technical stakeholders.

Requirements:

• Degree in a relevant quantitative discipline such as mathematics, statistics, physics, engineering or a related field.
• Strong foundation in statistical modelling and mathematical problem solving, applied to real world, noisy datasets.
• Proficiency in Python and MATLAB for data analysis and algorithm development.
• Demonstrated experience working on complex, technically demanding analytical problems, ideally involving physical, sensor derived or fault detection style data.
• Strong communication skills and the ability to work closely with a broader software and engineering team.

Desirable but not essential:

• Background in mechanical, structural or physical fault prediction or condition monitoring.
• Experience working with sensor derived or signal derived datasets in an industrial or infrastructure context.
• Exposure to deploying analytical models into a live production environment.