NxT Level
Our client is a public safety product that's looking to add a machine learning engineer to their team. This particular role is focused on NLP/NLU for their real-time audio translation team.
If you're excited about the future of video, audio, video content moderation, and more then you should reach out.
Core Challenges:
Mobile Video Streaming: The app is designed to ingest high-quality, low-latency video, transcode, and redistribute it to external media seamlessly. Radio Hardware: Development of proprietary software-defined radio-based devices to capture all radio dispatches in every major city, covering both analog and digital signals. Preferred Qualifications
A degree in Computer Science, Machine Learning, or a related field, or equivalent professional experience. A strong theoretical foundation in core Machine Learning concepts and techniques. Proficiency in fundamental principles of Linear Algebra, Statistics, and Probability. Experience with various ML techniques and frameworks, such as data discretization, normalization, sampling, linear regression, decision trees, SVMs, and deep neural networks. Familiarity with DL software frameworks like TensorFlow or PyTorch. At least 3 years of experience leading and implementing effective ML solutions in large scale production environments.
Technology Environment:
Go for Transactional Systems and Python for Analytic Systems running on a Kubernetes Infrastructure in GCP. Database is in Cassandra, MySQL, Redis, and Google PubSub. Web Services are build via React.js and Typescript.
If you're excited about the future of video, audio, video content moderation, and more then you should reach out.
Core Challenges:
Mobile Video Streaming: The app is designed to ingest high-quality, low-latency video, transcode, and redistribute it to external media seamlessly. Radio Hardware: Development of proprietary software-defined radio-based devices to capture all radio dispatches in every major city, covering both analog and digital signals. Preferred Qualifications
A degree in Computer Science, Machine Learning, or a related field, or equivalent professional experience. A strong theoretical foundation in core Machine Learning concepts and techniques. Proficiency in fundamental principles of Linear Algebra, Statistics, and Probability. Experience with various ML techniques and frameworks, such as data discretization, normalization, sampling, linear regression, decision trees, SVMs, and deep neural networks. Familiarity with DL software frameworks like TensorFlow or PyTorch. At least 3 years of experience leading and implementing effective ML solutions in large scale production environments.
Technology Environment:
Go for Transactional Systems and Python for Analytic Systems running on a Kubernetes Infrastructure in GCP. Database is in Cassandra, MySQL, Redis, and Google PubSub. Web Services are build via React.js and Typescript.