Machine Learning & Signal Processing Engineer
Engineering – Engineering
Are you passionate about Audio, Machine Learning and Music? Splice is building its Audio Science team in Los Angeles to create new technologies and product features that will empower over a million active users in their music creation process.
The ideal candidate will have a solid background in signal processing with hands on experience working on audio related projects and applied data science / machine learning. Some background in music is a plus!
Splice is changing the way musicians create, collaborate and distribute their work by connecting their creation process. We are growing quickly and we are hiring self-starters to make a massive impact on Splice and the producers that use our product. Splice is creating a brand new software paradigm (think dropbox meets github for music producers) and as such, we face difficult design decisions on a daily basis.We're co-founded by Steve Martocci (GroupMe) and well known open source programmer Matt Aimonetti (Sony PlayStation) both of whom you will work with on a daily basis. Splice is funded by Union Square Ventures along with True Ventures, SV Angel, First Round Capital, Lerer Ventures, Box Group.
As part of the Audio Science team you will:
- Design, implement and validate machine learning algorithms to better understand, classify, organize and recommend audio content from Splice’s massive database of sounds.
- Participate in the design and implementation of proof-of-concept prototypes that can unlock new, better and faster ways to create music with our product.
- Collaborate with Engineering and Product teams to support the integration and deployment of new product features coming from the Audio Science team.
- Participate in creative sessions to share your best ideas and help identify the most promising problems to work on, that maximize value to our users.
Things that we consider critical to becoming a great asset to our team:
- Great teamwork and communication skills: we work at the intersection of Science, Applied R&D, Engineering and Product; frictionless communication and mutual support are essential for the team to iterate quickly, discuss and refine directions when necessary, and deliver great results in short periods of time.
- Msc. in Electrical Engineering / CS with a solid background in Signal / Image Processing.
- Past projects in Audio Signal Processing.
- Experience designing and implementing classification / regression models using statistical methods and deep learning techniques.
- Experience in all stages of an applied machine learning project (dataset creation, cleanup, augmentation, feature extraction / feature engineering, model architecture, training, testing, deployment)
- Solid implementation skills in Python and familiarity working with NumPy, Pandas, Scikit-learn, Scipy, Keras / Tensorflow.