Machine Learning Engineer
vor 1 Monat
Nearmap is the Australian-founded, global tech pioneer innovating the location intelligence game.
Customers rely on Nearmap for consistent, reliable, high-resolution imagery, insights, and answers to create meaningful change in the world and propel industries forward.
Harnessing its own patented camera systems, imagery capture, AI, geospatial tools, and advanced Saa S platforms, Nearmap stands as the definitive source of truth that shapes the livable world.
Interested in building and executing geospatial numerical science on top of millions of square kilometres of aerial imagery?
This role is all about translating R&D from other parts of the Nearmap AI & Computer Vision team into data and ultimately, answers.
You will be working closely with a team of Data Scientists and ML Engineers to rapidly iterate on new ML-driven geospatial analytics products.
You'll be doing a mix of algorithmic implementation and validation as part of a full software engineering lifecycle.
The team focusses on algorithmic systems, where each node of the computational graph is a software application in its own right (such as a deep learning model or geospatial algorithm), and the goal is to produce outstanding high quality data for customers in a fully automated and cost effective manner.
We design, build, and operate software systems that take petabytes of data to transform aerial imagery to insight.
Our technology stack is based on the python scientific libraries and traverses machine vision deep learning technology such as Pytorch, and GIS tools such as the GEOS, Shapely and Geo Pandas libraries.
We work mostly in python for speed of development and occasionally drop down to compiled libraries when we need to care about performance.
Job Description A typical day will look like this Work within a team to deliver end-to-end technical solutions — typically starting with spike sessions, onto architectural design and test creation, iteration on the solution, measuring quality and ultimately deploying to production.
Participation in the design and scoping of greenfield projects.
Commitment to software best practices and a strong culture of peer review.
Skills We're after exceptional candidates, who have real world experience but are eager to learn.
Demonstrated history working in a numerical field: e.g.
computer vision, applied maths, physical sciences, geospatial analysis.
Strong approach to systems thinking, whilst remaining pragmatic.
Commitment to software engineering principles for scientific python, a keen eye for clean code, and a passion for robustness and correctness.
Working on shared codebases to produce production quality code.
Highly Desirable: Working with large data sets, where data doesn't fit into memory, and requires multiple nodes to compute efficiently.
A scientific mindset of formulating hypotheses, and applying statistical tests to validate them.
Working in a cloud-native environment using highly scalable compute.
Experience with operationalizing numerical applications and workflows.
Personal attributes Data science is a team sport; communicate well, share knowledge, and be open to taking on ideas from anyone in the team.
While extensive knowledge of theory and best practices are highly valued, pragmatism wins over elaborate theory when it comes to shipping products that work.
Qualifications Formal education in a field related to numerical science (Bachelor's degree in computer science, engineering, statistics, physics, etc.
).
Applicants with a Masters/Ph D will fit in well within the team, but are by no means necessary — we're more interested in what you can do
Additional Information Some of our benefits Quarterly wellbeing day off - Four additional days off a year as your "YOU" days.
Access to Linked In Learning.
Team Hackathons and Pitch-fests.
Wellbeing and technology allowance.
Nearmap subscription (of course
).
Stocked kitchen with access to all the sustenance you need.
In-office Lunch every Tuesday and Thursday at our Sydney CBD office.
Showers available for anyone cycling to work or lunchtime gym-goers
Working at Nearmap We move fast and work smart; often wearing multiple hats.
We adapted to remote working with ease and are continually looking at ways to improve.
We're proud of our inclusive, supportive culture, and maintain a safe environment where everyone feels a sense of belonging and can be themselves.
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 of our videos and find out more about what a day in the life at Nearmap looks like.
Please note: Successful candidate must have full working rights to work in Australia.
Unfortunately, we are unable to sponsor visas for overseas candidates at this stage.
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