Machine Learning Engineer

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 ** ** #J-18808-Ljbffr