Research Scientist - Audio

Transparency and accountability through machine learning
Today, officers spend 65% of their time dealing with paperwork, leaving only 35% of their time available to build deep relationships with the communities that they serve. Axon's mission is to improve transparency, accountability, and productivity in law enforcement via technology.

Axon Research's goal is to use deep learning to accelerate the many manual human workflows in public safety data, so that officers can spend more time with what matters most: the community. We focus on workflows, while continuing to entrust decision-making to officers.

Our product is a machine learning platform that uses deep learning to understand video footage and power three key use cases:
Accelerated footage review to enable supervisors to better understand their officers' behavior and provide positive or negative feedback in order to improve training and police/community relations.
Automated redaction to speed up the process of sharing footage with the public while protecting the privacy of citizens captured in video.
Automated reporting to populate factual records directly from video and audio, so officers can spend more time serving the community.

By removing the burden of manual notes and endless hours of keyboard work, officers can be present, build relationships and be more human. We want our technology to enable more personal interactions to help build safer and stronger communities.

How we do it
Axon Research is a group comprised of machine learning researchers and developers. We balance an aggressive roadmap of engineering challenges with a portfolio of research bets. All of our engineering work is in service of ML, and all of our research work will make major impacts to product if successful. We understand the importance of collaboration for rigorous research and our whole team is encouraged to publish and open-source. As the deep learning technology and team came from the acquisition of Dextro, we maintain a highly startup-centric culture.

Like Apple, Axon is a vertically-integrated company that builds the entire stack, from camera hardware to firmware to cloud evidence management solutions, and now all the way up to AI. This tight coupling enables incredible scale with machine learning across both training and inference.

Our work requires much more care than typical AI applications such as targeting ads or tagging photos; therefore, we are investing time into a whole new level of applied AI security, including understanding adversarial examples, as well as a wholly independent AI ethics board that guarantees that our work will be used only for transparency and accountability, never for surveillance.

The acoustic universe in which body worn cameras are immersed is teeming with life. Making sense of it all, with the goal of protecting that life, could be your job. Amidst the buzzing confusion of traffic, music, helicopters, and the occasional lawnmower, police officers and citizens converse. What is the tone and shape of their interaction? Does it escalate? Does it follow police department procedure. Though it's difficult and unsolved, the impact of our work is immense — unlike most ML research in industry, the success of your models directly impacts the status quo of transparency and accountability in law enforcement.

In this role,
You will design new models/techniques for understanding audio content.
You will pair with our research engineers to iterate on ideas as well as scale them out to production once they are ready.
You will continually read the state of the art of research and publish novel work yourself.
You will see that generalizable parts of your work get open-sourced.

Who you are:
Ideal Scientist would have done original deep learning research pertaining to audio (natural language processing, sound source separation, video diarization, summarization, captioning, etc).
Demonstrable research rigor via a PhD or a long publication track record in machine learning, natural language processing, signal processing, computer vision, robotics, or a similar field working with unstructured data
You have an unimpeachable grasp of deep learning theory and are comfortable designing new models, not just modifying existing ones.
You have a good understanding of the complete workflow from collecting and annotating the appropriate data for the task at hand to experimenting with and comparing different ML techniques to analyzing results.
You're able to execute on your ideas and have competence in frameworks like Tensorflow/Keras/Torch/Caffe/Darknet.
You care about getting to the “truth” of things and will dig deep to prove or disprove a hypothesis
We're building a team that spans diversity of all kinds, which we think is critical in our mission to bring officers and the communities they serve closer together. If this is something you care about, we would love to have you join us.

Compensation and Benefits

  • Medical/Dental/Vision
  • Company paid life insurance
  • Equity (RSUs)
  • Quarterly bonus 
  • Supportive parental leave policy
  • Unlimited paid-time-off
  • 401K with employer match
  • Commuter options
  • Full pantry
  • One of Seattle Business Magazine’s best companies to work for in 2017 and winner of Geekwire’s 2016 Geekiest Office Space
  • Opportunities to ride along with real US police officers in real life situations, see them use technology, and get inspired. 
  • And much more...

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Axon will also consider candidates for this role for our New York City office

Meet Some of Axon's Employees

Bryan W.

Vice President, Software Engineering

From building a top-talent team to creating cutting-edge technologies, Bryan and his co-workers are responsible for supporting Axon’s engineering efforts and experiences.

Surbhi S.


Surbhi writes the code that controls what Axon body cameras can do to help law enforcement officers out in the field during their day-to-day duties.

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