At Solid State AI we are developing an AI platform for semiconductor and aerospace manufacturers.Solid State AI is on a mission to infuse creativity and scientific rigor into the design, development and deployment of AI software products. Our objective is to create a software that is not only user-friendly and intuitive but also adds significant value to our manufacturing customers.Our clients are builders of products you use every day and our solution empowers them to increase production while reducing cost. At the heart of our offering is AIMS – AI for Manufacturing Systems.We are in search for a team member who not only understands the established principles of machine learning principles & practices and data science but is also prepared to challenge the norms. This mindset is crucial because the types of problems we are addressing requires creative thinking which leads to innovative solutionsJob Description:We are looking for a passionate and skilled Machine Learning Engineer to join our team. The ideal candidate will have expertise in developing and implementing supervised and unsupervised machine learning models. Examples of applications for these models are in anomaly detection, classification and regression tasks. You should be well-versed with packages such as Scikit learn, PyTorch, Dask, Ray and Optuna. You will be involved in the entire model development lifecycle, from data preprocessing to model deployment.Key Responsibilities:Design and implement supervised and unsupervised machine learning models especially in scenarios of big, small and sparse dataIdentify and recommend the optimal machine learning approach tailored to the specific characteristic of the data, including volume and veracity, and aligned with the intended use case of the modelWork with frameworks such as Dask, Optuna, and Ray to enhance model performance and scalabilityCollaborate with cross-functional teams to understand business requirements and provide ML solutionsConduct data analysis and preprocessing to prepare datasets for model trainingOptimize models for performance and accuracyStay up-to-date with the latest developments in machine learning and apply them to solve real-world problemsQualifications:Master’s /PhD degree in Computer Science, Statistics, Physics or a related fieldExpert in the foundations of tree-based and neural network algorithmsMinimum of 8 years of proven experience in machine learning, particularly with tree-based algorithms and neural networks such as: FNN, Autoenconders, Attention Mechanism and Transformers, RNN, GNNExpert in building custom loss functionsProficiency in PyTorch and experience with frameworks like Dask, Optuna, and Ray.Strong programming skills in PythonExcellent understanding of data structures, algorithms, and software engineering principlesExperience with big data technologies and cloud platforms is a plusStrong problem-solving skills and ability to work in a fast-paced environmentStrong communication skills to effectively convey complex conceptsAbility to work collaboratively in a team environmentExcellent analytical and problem-solving skillsBonus: Experience in manufacturing environment (Aerospace, Automotive, Semiconductor, Pharma or Chemical)#J-18808-Ljbffr