Senior Machine Learning Engineer

PICQUORA LLC · Anywhere · June 11, 2019

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💰 $120,000 - $180,000 / yr

At PICQUORA, our mission is to enable retailers to build loyal customers. We are a Bay Area Startup with funding from major investors and growing rapidly. Our goal is to build best in class AI+ML based Visual Search platform that helps retailers create game changing customer experiences. Interested in being part of ground breaking technology and building next generation solutions? Check out our openings.
 

We are looking for a senior developer/lead who has a strong interest and experience in Computer Vision and Deep Learning.

Responsibilities:

- Work in all the stages of building a state-of-the-art product, from creating prototypes, implementing and evaluating models to launching them in production.
- Provide guidance to junior engineers, suggest innovative solutions for acquiring/annotating data, training large datasets, improving model accuracy, and deploying at scale.
- Implement ML ideas published in recent papers on arXiv

Qualifications:

- Bachelors or Masters degree in Computer Science or equivalent.
- 5 years of programming experience.
- 3 years of industry experience in machine learning/deep learning.
- Experience in Python, PyTorch/TensorFlow/Keras, Object Detection and Recognition, Computer Vision.
- Experience with large datasets, multi-gpu training and production deployment.
- Should have participated in Kaggle competitions or implemented neural architectures proposed in research papers.   

 

Questionaire:

1. Where did you learn ML/Deep Learning? [If online, provide link to courses]
2. What specific neural networks are you familiar with?
3. What competitions have you participated on Kaggle?
4. What recent paper on arXiv made an impact on you/your work?
5. Would you able to implement neural architectures proposed in a research paper?
6. Which of the following have you worked on? [Yes/No]
a) Gradient Boost algorithm
b) Object Detection
c) Object Recognition
e) Object Segmentation
d) RNN/LSTM
e) Transfer learning
f) Cross-validation
g) Custom loss functions
h) production deployment of models

Acquiring Data Annotating Data Python Pytorch Tensorflow Keras Object Detection Computer Vision

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