|Job Type:||Full Time|
Nearmap is unique. An Australian global technology company with incredible people.
At Nearmap, we have petabytes of high quality aerial imagery (covering half a million square kilometres a year at 5-7cm resolution, and regularly captured imagery back to 2009). We produce automated 3D models of entire cities, and have recently launched our new product, Nearmap AI, which turns our visual content into semantic information to power decisions in a wide range of organisations. As a publicly listed company focussed on growth, we have both the resources to allow you to succeed in your role, and the agility (thanks to cloud-based infrastructure) to rapidly take advantage of the latest developments in the field. Nearmap is continually evolving, and you’ll need to thrive in a fast paced environment that changes rapidly.
We are looking for a Machine Learning Engineer to join our growing AI team team. You’ll be designing, building and operating software systems that manage petabytes of data, as we transform imagery with deep learning, and human expert labelling systems in the loop. Operating on mix of cloud and on-premise clusters, using cloud native technologies (primarily Kubernetes), you’ll make heavy use of technologies like Tensorflow, Keras, NVIDIA toolkits, Kubernetes, gRPC, and other modern MLOps solutions.
You will work with an incredibly passionate and talented team of Data Scientists and Machine Learning Engineers, and Data Engineers and your work will interface with systems that run tensorflow on thousands of GPUs, and train large deep learning models with novel architectures. You will report to the Principal Machine Learning Engineer who runs our Data Engine team within the AI Systems group. The team mixes data science, ML engineering and software engineering, and has responsibility for building the systems and managing the data for human expert labelling of our imagery, and support the training and management of our deep learning models. We have a custom labelling environment built on our Map Browser product that allows our labelling team to annotate 2D, 3D and source imagery. We use algorithms in the loop to enhance the human labelling of millions of images, and automatically produce training data sets for ML that continually grow and improve in quality as we seek to build a richer machine learning derived picture of our physical world. We are looking to build software and systems in place to facilitate efficient distributed and continual model training platform. This role will be critical in achieving that.
A typical day will look like this
- Completing end-to-end technical data science projects (taking design decisions, prototyping, putting into production).
- Reviewing code and work of peers.
- May be required to supervise associates.
- Minimum 4 years specific experience in machine learning (through any combination of an ML focused Masters or PhD, and relevant work experience)
- We're after an exceptional candidate, who has real world experience but is still eager to learn.
- Programming/Tech Environments: Ability to code in scientific python, using a linux environment, and git for source control. Understand the software engineer principles and passionate about good quality software engineering and machine learning code.
- Machine Learning: Strong grasp of machine learning fundamentals (regularisation, hyperparameter optimisation, validation methods)
- Scientific Approach: Follow the scientific method of formulating hypotheses, and applying statistical tests to validate them
- Software Engineering: Working on shared codebases to produce production quality code
- Cloud Computing: Working on AWS or GCP using distributed virtual machines, docker containers, etc.
- GP-GPU: Using GPUs to accelerate scientific computing
- MLOps: Experience with operationalizing ML applications and workflows
- Domain Knowledge – Computer Vision: Working on Machine Learning problems applied to image data
- Deep Learning: Applying modern artificial neural networks to solve machine learning problems
- Scale: Working with large data sets, where data sets don’t fit into memory, and require multiple nodes to compute efficiently
- Pragmatism: While extensive knowledge of ML theory is highly valued, pragmatism wins over elaborate theory when it comes to shipping products that work
- Collaboration: data science is a team sport, communicate well, share knowledge, and be open to taking on ideas from anyone in the team.
- Attention to detail: Showing attention to detail when it counts is important... to be considered for this role, [click this link](https://www.dropbox.com/s/u4janrzqgfxn61l/test.tar.gz?dl=0) and apply some basic data science skills (an astute software engineer should be able to solve it quickly with a little googling!).
- Formal education in a technical, data related field (Bachelor’s degree in computer science, engineering, statistics, physics, etc.)
Some of our benefits
Nearmap takes a holistic approach to our employees’ emotional, physical and financial wellness. Some of our current benefits include competitive pay, access to the Nearmap employee share scheme, short and long-term financial incentives, flexible working options, paid volunteer days, gym and phone rebates, and lots of development opportunities including hack-a-thons and pitch-fests.
If you can see yourself working at Nearmap and feel you have the right level of experience, we invite you to get in touch.
Watch some presentations on what we do in the AI Systems group: