Ikigai
Machine Learning Engineer
Ikigai, Cambridge, Massachusetts, us, 02140
Ikigai Labs seeks a dynamic and passionate engineer with strong software fundamentals to join a high-performing Machine Learning team. We are looking for a team player who is a quick learner, performs in a rapid development cycle, has a drive to surpass expectations, and an eagerness to share their work and knowledge. We encourage applicants from all backgrounds and communities. We are committed to having a team that is made up of diverse skills, experiences, and abilities.Role
Optimize and deploy ML solutions for maximum performance and scaleBuild productivity tools and services for the ML platform, which includes working on various tools like Kubernetes, Helm, EKS etcStrong understanding of deep learning model architectures such as convolutional, residual, attentional, and recurrent neural networksAbility to understand recent ML and deep learning literature and adapt those models to solve real world problemsWork collaboratively to develop and integrate AI and machine learning that deliver on business valueWork with large datasets and build a ML pipeline to process and train the dataDesign and develop scalable data integration (ETL/ELT) processesDesign and develop an on-demand predictive modeling platform with gRPCUtilize Kubernetes to orchestrate the deployment, scaling and management of Docker containersUtilize and learn various Cloud services - AWS, Azure etc to solve cloud-native problemsProvide periodic support to our customer success teamTechnologies
Languages: Python3, C++, Rust, SQLFrameworks: PyTorch/TensorFlow, DockerDatabases: Postgres, Elasticsearch, DynamoDB, RDSCloud: Kubernetes, Helm, EKS, Terraform, AWSData Engineering: Apache Arrow, Dremio, RayMisc.: Git, Jupyterhub, Apache Superset, Plotly DashQualifications
1-3 years of experience with a bachelor's degree in Computer Science, Math, or Engineering; or a master's degree in related fieldUnderstanding of data structures, data modeling, algorithms and software architectureKnowledge of probability, statistics and algorithmsExperience with Machine learning and Deep learning libraries such as: Scikit Learn, Keras, TensorFlow, PyTorch, Theano, or DyLib(bonus) Experience with big data and distributed computing technologies such as: Hadoop, MapReduce, Spark, and StormExperience with Python, AWS services, and/or ETL/ELT pipeline experiencesUnderstanding of key software design principlesExperience with Kubernetes and/or EKS (optional)Understanding of the fundamentals of design patterns and testing best practicesThe ability to learn quickly in a fast-paced environmentExcellent organizational, time management, and communication skillsThe desire to work in an AGILE environment with a focus on pair programmingWillingness to discuss obstacles, find creative solutions, and take initiativeThe ability to receive and give both constructive and encouraging feedback
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Optimize and deploy ML solutions for maximum performance and scaleBuild productivity tools and services for the ML platform, which includes working on various tools like Kubernetes, Helm, EKS etcStrong understanding of deep learning model architectures such as convolutional, residual, attentional, and recurrent neural networksAbility to understand recent ML and deep learning literature and adapt those models to solve real world problemsWork collaboratively to develop and integrate AI and machine learning that deliver on business valueWork with large datasets and build a ML pipeline to process and train the dataDesign and develop scalable data integration (ETL/ELT) processesDesign and develop an on-demand predictive modeling platform with gRPCUtilize Kubernetes to orchestrate the deployment, scaling and management of Docker containersUtilize and learn various Cloud services - AWS, Azure etc to solve cloud-native problemsProvide periodic support to our customer success teamTechnologies
Languages: Python3, C++, Rust, SQLFrameworks: PyTorch/TensorFlow, DockerDatabases: Postgres, Elasticsearch, DynamoDB, RDSCloud: Kubernetes, Helm, EKS, Terraform, AWSData Engineering: Apache Arrow, Dremio, RayMisc.: Git, Jupyterhub, Apache Superset, Plotly DashQualifications
1-3 years of experience with a bachelor's degree in Computer Science, Math, or Engineering; or a master's degree in related fieldUnderstanding of data structures, data modeling, algorithms and software architectureKnowledge of probability, statistics and algorithmsExperience with Machine learning and Deep learning libraries such as: Scikit Learn, Keras, TensorFlow, PyTorch, Theano, or DyLib(bonus) Experience with big data and distributed computing technologies such as: Hadoop, MapReduce, Spark, and StormExperience with Python, AWS services, and/or ETL/ELT pipeline experiencesUnderstanding of key software design principlesExperience with Kubernetes and/or EKS (optional)Understanding of the fundamentals of design patterns and testing best practicesThe ability to learn quickly in a fast-paced environmentExcellent organizational, time management, and communication skillsThe desire to work in an AGILE environment with a focus on pair programmingWillingness to discuss obstacles, find creative solutions, and take initiativeThe ability to receive and give both constructive and encouraging feedback
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