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Machine Learning Engineer
2 months ago
*Please note - this is an evergreen role*
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.
- Intrinsic motivation …driven by autonomy, goal clarity, and regular feedback.
- Commit to desired outcomes …define desired outcomes and achieve them vigorously.
- No egos, no jerks …no joke.
You will be responsible for:
- Expertise in ML frameworks and common ML libraries (TensorFlow, Keras, PyTorch, etc.).
- Expertise in data manipulation and analysis skills. Should be comfortable with affiliated libraries (Pandas, NumPy, SciPy, etc.).
- Understanding of a wide range of ML algorithms (SVMs, neural networks, clustering algorithms, etc.) and ensemble methods.
- Experience with selecting appropriate models for various tasks, and understanding of how to improve models given their performance evaluations.
- Expertise in feature selection, feature extraction, and feature engineering.
- Deep learning expertise (knowledge of designing and implementing multiple neural network architectures, experience with transfer learning techniques, proficiency in tuning hyperparameters).
- Data science skills (data analytics and visualization).
Qualifications:
- 3 years relevant industry experience with Machine Learning, Statistics, Data Engineering, or similar
- Experience with device development, robotics preferred
- Strong understanding of data structures or algorithms
- Familiar with hybrid models that integrate multiple data sources
- Experienced with FCNNs (fully connected neural networks) and pre-trained CNNs
- Comfortable with feature engineering, especially for unconventional classification models
- Experienced with a wide range of modeling approaches such as SVMs and clustering algorithms
Preferred skills:
- Experience taking ideas from inception to launch
- Understanding of product market fit, user experience, analytics, metrics and testing
- Experience working with highly scalable, fault-tolerant, secure and compliant architecture and systems
- Strong communication skills and bias for action
- Familiar with holographic data processing
