Gray Matters Inc. is a disruptive technology company that is looking for talented, motivated individuals to help us grow. Our purpose-built, enterprise-grade blockchain platform is changing how companies view, manage, and protect their assets in very complex and challenging supply chains.
We are seeking a Machine Learning Engineer to join our team and help design and develop advanced machine learning models and algorithms tailored to specific business problems. The ideal candidate will be a self-driven individual with experience who can work across a cross-functional team to implement machine learning solutions into production systems.
- Design, develop, and deploy advanced machine learning models and algorithms tailored to specific business problems.
- Collaborate with cross-functional teams to integrate machine learning solutions into production systems.
- Analyze and preprocess large-scale IoT data, ensuring data quality and relevance for model training and evaluation.
- Utilize AWS services for efficient model deployment, scaling, and monitoring.
- Continuously research and stay up to date with the latest machine learning techniques and best practices to improve existing solutions and develop new ones.
- Contribute to the overall architecture and design of machine learning systems.
- Participate in code reviews and ensure high-quality code through adherence to best practices and industry standards.
- Provide technical leadership and mentorship to junior team members.
- Must be a full U.S. citizen.
- Bachelor’s or master’s degree in computer science, Engineering, or a related field.
- 5+ years of experience in machine learning, with a strong foundation in algorithms, statistics, and probability.
- Proficiency in Python and experience with machine learning frameworks such as TensorFlow, PyTorch, or Keras.
- Demonstrated experience working with IoT data, including data collection, preprocessing, and feature engineering.
- Hands-on experience with AWS services, such as SageMaker, EC2, S3, and Lambda, and familiarity with the AWS ecosystem.
- Strong knowledge of various machine learning techniques, including supervised, unsupervised, and reinforcement learning.
- Experience with data visualization tools, such as Matplotlib, Seaborn, or Plotly.
- Excellent problem-solving, critical thinking, and communication skills.
- Ability to work independently and as part of a team in a fast-paced environment.
- Experience with IoT platforms.
- Knowledge of big data technologies, such as Apache Spark, Hadoop, or Kafka.
- Familiarity with containerization technologies, such as Docker and Kubernetes.
- Experience with version control systems, such as Git.
- Track record of contributions to open-source projects or publications in relevant conferences and journals.
Other Key Details
We are a bunch of self-starters who thrive in an environment where the path has not yet been paved. We love to innovate and collaborate and are never content until we get it right. We don’t take ourselves too seriously, but we are very serious about the work we do. Integrity is very important to us, and we will never abandon a promise or commitment. We want to be the best in the marketplace and are always looking for talented, driven individuals to help us achieve this goal. Our benefits include:
- Full medical and dental benefits
- Unlimited PTO
- Hybrid work location possible
- Little hierarchy and the ability to work with colleagues at all levels and across disciplines
- An opportunity to get into a growing company on the ground floor
About Gray Matters
Gray Matters Inc. (GMI) is a technology start-up focused on delivering SaaS-based supply chain management solutions that are powered by blockchain, to federal and commercial customers. Initially developed to secure a complex international supply chain for a global U.S. Government national security organization, the Maverix® platform is unmatched in the industry for its ease of use, end-to-end visibility, blockchain-enabled data transparency and integrity, and high level of security, including post-quantum resistant encryption.