Machine Learning and Data Processing Specialist

• Analytics for the Australian Grains Industry (AAGI)

• Spearhead ground breaking advancements in data engineering by leveraging cutting-edge machine learning techniques

• Strong commitment and action toward a diverse and inclusive workforce

• Access discounts across health and fitness, travel, retail, tech + more

• Based at the St Lucia Campus

About UQ

As part of the UQ community, you’ll have the opportunity to work alongside the brightest minds, who have joined us from all over the world.

Everyone here has a role to play. As a member of our professional staff cohort, you will be actively involved in working towards our vision of a better world. By supporting the academic endeavour across teaching, research, and the student life, you’ll have the opportunity to contribute to activities that have a lasting impact on our community.

Join a community where excellence is at the core of our culture, contributions are valued and a range of benefits and rewards are available, such as:

• 26 weeks paid parental leave or 14 weeks paid primary caregiver leave

• 17% superannuation contributions

• 17.5% annual leave loading

• Access to flexible working arrangements including hybrid working options, flexible start/finish times, purchased leave, and a condensed fortnight

• Health and wellness discounts – fitness passport access, free yearly flu vaccinations, discounted health insurance, and access to our Employee Assistance Program for staff and their immediate family

• Salary packaging options

About This Opportunity 

The Machine Learning and Data Processing Engineer is a senior professional specialist in their field of science, scientific research or research methodology. They work as part of a research team to provide high-level, specialist expertise to scientific research projects, programs, and initiatives.  Enabling and supporting research, this position will play a pivotal role in the complete process of creating, deploying, and maintaining machine learning solutions. This work will ensure these solutions are accurate, robust, scalable, and smoothly integrated within our infrastructure.

We are looking for an experienced and highly motivated Machine Learning and Data Processing Engineer to support the academics and higher degree research students. This role requires a profound understanding and hands-on experience with image data and various data modalities in the context of machine learning. The incumbent will provide technical expertise in delivering data and analytical services, including data ingestion, transformation, storage, and dashboarding.

In this role, you will be committed to advancing our data engineering and operations capabilities, fostering innovation and efficiency in our processes. This position is vital in our dedication to leveraging data and analytics to drive research outcomes and academic excellence.

Key responsibilities will include: 

• Establish machine learning solutions with your expertise.

• Design and implement data science solutions to address complex data science problems.

• Improve the deployment process of machine learning models to production environments, ensuring seamless integration into existing systems.

• Develop strategies for scaling machine learning solutions to efficiently handle large datasets and increased workloads.

• Implement real-time monitoring solutions to track the performance of deployed machine learning models and data pipelines, including setting up alerts for anomalies or issues.

• Oversee the entire machine learning model lifecycle, including model retraining, updates, and maintenance.

• Design, build, and optimize data pipelines that collect, preprocess, and transform data for machine learning tasks, with a focus on complex data types such as images and multiple modalities.

• Continuously fine-tune machine learning models and data pipelines for optimal performance and resource utilization.

• Responsible for maintaining comprehensive documentation of data processing and machine learning workflows, including implementing version control practices, to facilitate access, understanding and collaboration among team members (and conforming to the University's data policies and procedures ).

This is a full-time (100%), fixed-term position for up to 1 year.

At HEW level 8, the full-time equivalent base salary will be in the range $108,975.08 to $122,176.31, plus a generous super allowance of up to 17%. The total FTE package will be up to $127,500.84 - $142,946.29 annually. As this role is covered by an Enterprise Agreement, you will also receive regular remuneration increases – at least once a year.

About You  

• PhD degree in Machine Learning, Computer Science, Data Science, or a related discipline with experience (3 to 5 years) in developing ML solutions.

• Deep understanding of machine learning algorithms and principles, and the ability to apply them in real-world situations.

• Proven experience in deploying and scaling machine learning models in a production environment.

• Proficiency in programming languages commonly used in data science and machine learning, such as Python, R, or Java.

• Experience in using version control systems and source code management repositories

• Experience with database management systems, ETL tools, and data pipeline technologies.

• Familiarity with cloud platforms and understanding of how to deploy and scale machine learning models in the cloud.

• Experience in designing and optimizing data pipelines, especially for complex data types like images.

• Excellent written and verbal communication skills, with the ability to maintain clear documentation and collaborate effectively with cross-functional teams.

• Strong problem-solving skills, with the ability to implement monitoring solutions and fine-tune models for optimal performance.

• Demonstrated ability to stay informed about the latest trends and best practices in the field.

In addition, the following mandatory requirements apply:

• Work Rights: You must have unrestricted work rights in Australia for the duration of this appointment to apply. Visa sponsorship is not available for this appointment. 

• Mandatory Immunisations: It is a condition of employment for this role that you will be required to provide evidence of immunisation against certain vaccine preventable diseases.

• Background Checks: All final applicants for this position may be asked to consent to a criminal record check. Please note that people with criminal records are not automatically barred from applying for this position. Each application will be considered on its merits.

Questions? 

For more information about this opportunity, please contact Prof Jason Ferris . For application queries, please contact [email protected] stating the job reference number (below) in the subject line. 
 

All applicants must upload the following documents in order for your application to be considered:

• Cover letter addressing the ‘About You’ section  

• Resume 

Other Information 

At UQ we know that our greatest strengths come from our diverse mix of colleagues, this is reflected in our ongoing commitment to creating an environment focused on equity, diversity and inclusion .  We ensure that we are always attracting, retaining and promoting colleagues who are representative of the diversity in the broader community, whether that be gender identity , LGBTQIA+ , cultural and/or linguistic , Aboriginal and/or Torres Strait Islander peoples , or people with a disability . Accessibility requirements and/or adjustments can be directed to [email protected] .

If you are a current employee (including casual staff and HDR scholars) or hold an unpaid/affiliate appointment, please login to your staff Workday account and visit the internal careers board to apply for this opportunity. Please do NOT apply via the external job board.

Applications close Monday 25th March 2024 at 11.00pm AEST (Job Reference Number - R-35308). Please note that interviews have been tentatively scheduled for Thursday 28th March.

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