Research Engineer (Machine Learning)


San Francisco, CA, US
  • Job Type: Full-Time
  • Function: Data Science
  • Post Date: 01/01/2021
  • Website:
  • Company Address: 44 Tehama Street, San Francisco, CA, 94105

About Navisens

Navisens™ motionDNA™ locates mobile devices indoors, underground & outdoors with zero infrastructure. No WiFi. No GPS. No beacons. Winner Best Tech @Launch 2013.

Job Description


The Machine Learning Research Engineer position is a full-time position. We are looking for individuals that are extremely creative, can move quickly and are not afraid to tackle any problem that comes their way. Both capacity to deliver and a strong analytical basis are necessary. Having intellectual curiosity is also key, since many times you will have to work in cross-disciplinary projects. We value your degree, but we are not obsessed with it. Show us your talent and knowledge throughout the interview process, disregarding your formal background, and you will be in. 

As a researcher in a high-impact startup you will also have the possibility of becoming a thought leader in the field through white papers, publications in peer-reviewed journals and IP creation.


Minimum Qualifications:

Probability and Statistics theoretical knowledge.

Fundamentals of Optimization methods.

Familiarity with Applied Machine Learning (show us your Github!), and its challenges.

Theoretical understanding and command of Machine Learning main classification and regression techniques (ANN “deep” or not, SVM, Forests, Decision-trees, Bayes, Logistic, etc...)

Perfect command of Matlab or Python for prototyping ideas. Good command of both. In other words: Perfect(Matlab||Python) && Good(Matlab&&Python)

Familiarity with mainstream tools and libraries like TensorFlow and Keras.

Capacity to build end to end pipelines to test models. That will involve at least a basic command of SQL and databases.

Be able to design test experiments and metrics to anticipate deployment challenges and track performance evolution. Further than training models.

Analytical rigor. Deliverables but founded on a solid approach; patches don’t last long.

Ability to communicate with peers and work in a team.

Desired Qualifications:

models, preferable mobile systems., leveraging performance with model size and computational efficiency. Real-time latency constraints.

Machine learning methods for feedback systems. Parameter and state estimation.

Familiarity with online learning methods. Reinforcement Learning.

Familiarity with time-dependent systems and different Machine Learning approaches to these systems (Recurrent NNs, LSTM, etc...)

Augmentation techniques, co-training, auto-encoding and other strategies to deal with limited-size data sets.

C/C++ programming experience.

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Disclaimer: Local Candidates Only
This company does NOT accept candidates from outside recruiting firms. Agency contacts are not welcome.