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 solutions
Job 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 data
Identify 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 model
Work with frameworks such as Dask, Optuna, and Ray to enhance model performance and scalability
Collaborate with cross-functional teams to understand business requirements and provide ML solutions
Conduct data analysis and preprocessing to prepare datasets for model training
Optimize models for performance and accuracy
Stay up-to-date with the latest developments in machine learning and apply them to solve real-world problems
Qualifications:
Master’s /PhD degree in Computer Science, Statistics, Physics or a related field
Expert in the foundations of tree-based and neural network algorithms
Minimum 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, GNN
Expert in building custom loss functions
Proficiency in PyTorch and experience with frameworks like Dask, Optuna, and Ray.
Strong programming skills in Python
Excellent understanding of data structures, algorithms, and software engineering principles
Experience with big data technologies and cloud platforms is a plus
Strong problem-solving skills and ability to work in a fast-paced environment
Strong communication skills to effectively convey complex concepts
Ability to work collaboratively in a team environment
Excellent analytical and problem-solving skills
Bonus: Experience in manufacturing environment (Aerospace, Automotive, Semiconductor, Pharma or Chemical)