A day in the life of a machine learning engineer

Grant and Sam

Sam Bahrami and Dr Grant Osborne in the AIML Laboratory

Story written by Dr Sarah Keenihan, AIML

Sam Bahrami is a machine learning engineer at the Australian Institute for Machine Learning (AIML), the University of Adelaide. 

It won’t blow your mind to know that checking emails is the first task on his list when he arrives at the AIML building, Lot Fourteen for work each day. 

It what happens afterwards that’s way more interesting. 

“Well…actually, coffee comes next,” Sam says. “But then I move on to solving problems.” 

Sam works with Principal Machine Learning Engineer Dr Grant Osborne and other colleagues at AIML to deliver technology solutions for partner industries, including businesses in the transport, health and medical, agriculture and technology sectors. 

Working creatively, and in teams

The stereotypical view of a technology whiz working alone, staring at a screen all day doesn’t apply here. 

“We often work in small groups, and it’s quite collaborative,” Sam says. “It’s creative too, because we’re designing and planning how to solve problems that have no clear best solution.”

Sam uses code to write instructions that computers can understand – this is called programming. A series of instructions put together to accomplish a specific task is an algorithm. 

In machine learning and artificial intelligence (AI), algorithms are combined under precise operating conditions to create a computer model that can be trained with data. 

“So for example, we can use medical data to train a computer model to detect an abnormality in an X-ray,” Sam says. “We use different data sets to train and test each model.”  

People skills are vital 

Good communication skills are important when you’re an AIML machine learning engineer, as regular meetings with clients are a core activity.  

“We discuss what progress has been made, and identify the next steps in a project,” Sam says. “They have to articulate what they need, and we explain what’s possible in terms of technology, and then carry it out.” 

AIML Principal Machine Learning Engineer Grant Osborne agrees. 

“The more you work with clients, the more you realise the interactions with people are as important as the technology,” Grant says. “We have to find out what our clients really want, and then translate that down into tasks that get delivered.” 

“The key is in listening to the client properly and keeping in touch with them as the work progresses,” says Grant. 

 

Tapping into a pool of expertise

Including Grant and Sam, there are 9 programmers and engineers working in AIML’s Engineering team. 

The group also taps into the expertise of research associates in the AIML building, and more broadly from the University of Adelaide when additional skills are required. 

“We have a core group of researchers here that I know are very solutions-focused,” Grant says. “I pull them into my team to advise on the latest models most suited to a particular client problem.” 

For example, Grant’s team collaborated with remote sensing expert Dr Kenneth Clarke on a project for PIRSA (the South Australian government’s Department of Primary Industries and Regions).  The work involved developing a new automated tool that has the capability to assist land condition assessment across South Australian Pastoral Leases using satellite remote sensing, geospatial data analysis and machine learning. The tool is referred to as CARMS: Condition Assessment and Risk Management System. 

“Working together, we built a system from first principles, using satellite data to highlight anomalies in land used to graze animals,” Grant says. “It’s a great example of creating practical solutions to real problems through bringing together academic and industry expertise.”  

 

The Australian Institute for Machine Learning is recognised as one of the top artificial intelligence and computer vision research institutions globally. Whether you are a student, researcher or corporation, read more about how we can work together here. 

Tagged in Machine Learning, Partners, Agriculture