Senior Machine Learning Engineer

About the role

Machine learning work shapes how podcast creation experiences improve for millions of users. Cross functional teams design systems and infrastructure that power personalized recommendations and content impact.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Improve large scale machine learning methods to strengthen podcast creation workflows, guided by product and research objectives. Collaboration with research scientists, data scientists, and engineers defines model requirements and measures success for long-term user satisfaction.

Enhance audio and text capabilities inside the ML team, using signal processing expertise to refine representations and downstream performance. Build, operate, and iterate on production ML systems with clear interfaces that advise and enrich Zencastr services.

Requirements

The posting states a minimum of 3 years of experience.

Demonstrate required years of experience in Python to ensure fluency in writing , maintainable code. Bring proven machine learning research experience focused on speech to guide model choices and evaluation strategies. Show hands on training and deployment of neural networks across training and serving environments.

Use machine learning libraries such as PyTorch, Tensorflow, and Scikit-Learn to implement and test experiments. Operate and build production ready ML systems, including monitoring and reliability practices. Lead the design and implementation of major software components, systems, and features end to end.

Rapidly build model prototypes to validate ideas against data and user behavior. Work with cloud technologies such as Google Cloud, AWS, and Modal to deploy scalable training and inference pipelines. Drive independent projects and form partnerships to solve large multi functional efforts with minimal direct oversight.

Nice to have

Publish in peer reviewed journals to share findings with the broader research community. Work with modern speech processing frameworks to integrate cutting edge models. Practice strong dev ops skills for reliable ML pipelines and deployment. Apply advanced DSP experience to improve feature quality and robustness. Use databases such as MongoDB or SQL for data storage and analysis. Write unit, integration, and load tests to validate model and system behavior. Build APIs to connect ML components with front end and backend services. Implement Docker containers and container clustering with Kubernetes for scalable execution.

Practical notes

The role is based in the New York Office. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Machine learning roles rely on strong math foundations and experimentation. Speech processing intersects audio signals, language models, and user behavior metrics. Production ML systems require testing, containerization, and cloud orchestration. Data pipelines, model training, and deployment workflows are central to daily work. Teams often combine research experiments with scalable software delivery practices.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.