Machine Learning Engineer

at  Unlimit Ventures

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.

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

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.

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).