**Please note - Actively hiring for this position in the San Diego, CA area**
As a Machine Learning Engineer, you will play a critical role in developing cutting-edge models and algorithms that transform data into actionable insights. You will collaborate with cross-functional teams, work on challenging projects, and work building one of our new exciting products in the environmental devices industry. This role is a contractor role, with strong opportunities for permanent hiring and growth.
If you have a strong foundation in machine learning frameworks, data manipulation, and model optimization, and are eager to apply your skills in a fast-paced, innovative environment, we would love to hear from you.
Our values:
- Prudent optimism …glass-half-full, with a dose of caution to challenge our assumptions.
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Intrinsic motivation …driven by autonomy, goal clarity, and regular feedback.
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Commit to desired outcomes …define desired outcomes and achieve them vigorously.
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No egos, no jerks …no joke.
You will be responsible for:
- Expertise in ML frameworks and common ML libraries (TensorFlow, Keras, PyTorch, etc.).
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Expertise in data manipulation and analysis skills. Should be comfortable with affiliated libraries (Pandas, NumPy, SciPy, etc.).
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Understanding of a wide range of ML algorithms (SVMs, neural networks, clustering algorithms, etc.) and ensemble methods.
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Experience with selecting appropriate models for various tasks, and understanding of how to improve models given their performance evaluations.
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Expertise in feature selection, feature extraction, and feature engineering.
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Deep learning expertise (knowledge of designing and implementing multiple neural network architectures, experience with transfer learning techniques, proficiency in tuning hyperparameters).
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Data science skills (data analytics and visualization).
Qualifications:
- 2 to 5 years relevant industry experience with Machine Learning, Statistics, Data Engineering, or similar
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Experience with device development, robotics preferred
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Strong understanding of data structures or algorithms
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Familiar with hybrid models that integrate multiple data sources
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Experienced with FCNNs (fully connected neural networks) and pre-trained CNNs
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Comfortable with feature engineering, especially for unconventional classification models
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Experienced with a wide range of modeling approaches such as SVMs and clustering algorithms
Preferred skills:
- Experience taking ideas from inception to launch
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Understanding of product market fit, user experience, analytics, metrics and testing
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Experience working with highly scalable, fault-tolerant, secure and compliant architecture and systems
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Strong communication skills and bias for action
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Familiar with holographic data processing
