Machine Learning Engineer
Apton is looking for a Machine Learning Engineer to gather training data, run regressions and train, verify inferences and other related experiments. They will also support the development of Intellectual Property (e.g. patent application or kept as trade secret). The successful candidate will have demonstrated capability as a Software Engineer resulting in the successful commercial launch of products.
- Masters or PhD in Computer Science, Electrical Engineering, Computer Science, Statistics/Mathematics, or related discipline.
- 2+ years’ Experience with deep learning methods, including CNN, RNN, and etc.
- 2+ years’ Experience with classical machine learning and statistical techniques, including regression, Bayesian methods, regularization, boosting, ensembles, support vector machines, etc.
- Familiar with existing machine learning tools or libraries such as scikit-learn, numpy, scipy, pandas, liblinear, libsvm, etc.
- Familiar with existing deep learning tools or libraries (Tensorflow, Keras, Torch, Theano, and/ or Caffe).
- Software languages – C/C++, Matlab
- Knowledge of imaging, signal processing, and computer vision would be a plus
The successful candidate will have:
- Actively involved in technical discussion/presentation and provide input to other members from hardware, instrument control and system engineering teams.
- Comfortable in an agile
- Solid troubleshooting skills
- Proven ability to effectively manage multiple activities simultaneously
- Demonstrated ability to drive data-based decision making
Information for recruiters
Apton Biosystems will only pay a fee for candidates submitted or presented where there is a signed recruiting agreement in place between Apton and the recruiter or agency prior to the submittal and the candidate is submitted for a specific requisition as requested by Apton. In the case of candidate(s) submitted or presented to Apton by a recruiter or agency without a signed agreement, Apton explicitly reserves the right to pursue and hire those candidate(s) without any financial obligation to the recruiter or agency.