
Senior Machine Learning Engineer (Remote)
Remote · USFull-timePosted 1y agoStill listed 3w ago
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Job overview
Zencastr is hiring a Senior Machine Learning Engineer (Remote). Zencastr is seeking a Senior Machine Learning Engineer to join their team, focusing on building end-to-end AI systems, core microservices, and infrastructure. This role involves working on large-scale machine learning to enhance the podcast creation experience, collaborating with research scientists, data scientists, and other engineers to prototype and productize new ML solutions at the intersection of speech processing and user satisfaction.
Key focus areas include Design, research and develop state-of-the-art machine learning applications and algorithms, Collaborate with engineering partners and colleagues in large multi-functional efforts, and Drive forward audio and text capabilities within the ML team.
Successful candidates bring Postgraduate Degree In Machine Learning, Postgraduate Degree In Mathematics, and Postgraduate Degree In Computer Science. Important skills include Machine Learning, Python, Speech Processing, Neural Networks, PyTorch, and TensorFlow. Preferred (not required): Dev Ops, Advanced DSP, MongoDB, and SQL.
Skills & qualifications
Skills
Qualifications
Full job description
The Role
We build end-to-end AI systems, core microservices, and infrastructures by leveraging cutting-edge technologies to impact millions with personalized user experiences everyday.
We are looking for an outstanding machine learning engineer to join our team! The role will provide an opportunity to work on large scale machine learning to improve the podcast creation experience at Zencastr.
You’ll join a team of hardworking engineers that are passionate about understanding what drives users’ long-term happiness with Zencastr, and how our recommendations and content affects that. As an integral part of the squad, you will collaborate with research scientists, data scientists and other engineers in prototyping and productizing brand-new ML at the intersection of speech processing and long-term user satisfaction.
What You'll Do..
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Design, research and develop state-of-the-art machine learning applications and algorithms to improve the lives of podcasters and podcast listeners around the world
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Collaborate with engineering partners and colleagues in large multi-functional efforts to build new product features that advise and enrich Zencastr’s various ML services.
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Drive forward audio and text capabilities within the ML team
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Work with some of the brightest minds in signal processing
If This Describes You..
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A postgraduate degree in Machine Learning, Mathematics, Computer Science, or a related quantitative field
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5 + years experience in Python
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3+ years experience in machine learning research, with a focus on speech
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3+ years experience training or deploying neural networks
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Significant experience with machine learning libraries like PyTorch, Tensorflow, Scikit-Learn
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Experience with building and operating production-ready ML systems
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Ability to lead design and implementation of major software components, systems, and features
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You enjoy quickly building model prototypes
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Experience with cloud technologies (Google Cloud, AWS, Modal)
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You are a self-starter who drives your own projects and builds positive relationships with partners and colleagues to solve big multi-functional efforts from start to finish. You are able to succeed with minimal mentorship and process.
Bonus points if you have:
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Publications in peer-reviewed journals from a related field
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Experience with modern speech processing frameworks
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Good dev ops experience
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Advanced DSP experience
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MongoDB or SQL experience
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Experience with unit, integration, and load testing
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Experience building APIs
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Experience with Docker containers, Implementing Docker Containers, Container Clustering
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Experience with container orchestration technology such as Kubernetes a big plus
How to apply:
Apply at https://jobs.ashbyhq.com/zencastr/c6a9c3b0-f260-4820-bb97-e78142eabd1e
We are flexible! For the right candidate we ask: What do you need to do your best work?
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