Principal Machine Learning Engineer
vor 2 Wochen
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 SaaS 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?
Job Description 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 responsible for the people, processes, and algorithmic systems for creating conflated Map Data products.
Conflation is the process of combining multiple data observations about the world into a single, cohesive map that prioritises usability, practicality, and a straightforward representation of real world objects (rather than explicitly modelling individual sources of data observations).
Input data sources include aerial imagery (multiple surveys over time, 2D, 3D, multi-angle, and captured by multiple providers), other geospatial sources such as property data, permit data or suitably licensed open data sets.
Products are likely to conflate multiple datasets of varying degrees of quality and cleanliness across spatial and temporal axes.
A key challenge is to provide transparency of data provenance and accuracy, while abstracting away as much complexity as possible.
We design, build, and operate software systems that take petabytes of data to transform aerial imagery to insight.
The team focuses 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.
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 GeoPandas libraries.
We work mostly in python for speed of development and occasionally drop down to compiled libraries when we need to care about performance.
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.
Lead a small team both as a tech and people leader.
Participation in the design and scoping of greenfield projects with a high degree of interaction with product teams.
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 10+ year history working in a numerical field: e.g.
computer vision, applied maths, physical sciences, geospatial analysis.
A developed ability to lead people, including good emotional intelligence and a focus on business outcomes.
Working on shared codebases to produce production quality code.
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.
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/PhD 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 Nearmap takes a holistic approach to our employees' emotional, physical and financial wellness.
Some of our current benefits include:
Quarterly wellbeing day off - Four additional days off a year as your "YOU" days.
Access to LinkedIn 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.
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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