Senior Machine Learning Engineer

About the role

About Deploy

Deploy is an intelligence infrastructure platform that gives developers and enterprises access to open source and proprietary AI models. For leading open source LLMs, Deploy operates an optimized model inference engine that keeps costs and latency low while maintaining high throughput. Deploy also offers a set of proprietary AI models that address challenges in content safety, AI & deepfake detection, sponsorship intelligence, and IP & publicity protection. Trusted by the world's largest and most innovative organizations, Deploy's solutions unlock high-performance AI model inference at production scale.

Deploy has raised more than $200M from leading investors, including Tomales Bay Capital, General Catalyst, 8VC, Glynn Capital, Bain & Company, Visa Ventures, and others. The company has more than 250 employees across offices in San Francisco, Seattle, New York, and Delhi.

Senior Machine Learning Engineer

In order to execute our vision, we need to grow our team of best-in-class machine learning engineers. We are looking for developers who are excited about staying at the forefront of deep learning technology, prototyping state-of-the-art neural net models and launching these models into production. We value hard workers who have no qualms working with terabyte-scale datasets, who are interested in learning new technologies at all levels of the machine learning stack, and who move fast and take ownership of their projects. Our ideal candidate has experience creating a working machine learning-powered project from the ground up, contributes innovative ideas and ingenious implementations to the team, and is capable of planning out scalable, maintainable data pipelines.

Responsibilities - Everything involved in applying a ML model to a production use case, including, designing and coding up the neural network, gathering and refining data, training and tuning the model, deploying it at scale with high throughput and uptime, and analyzing the results in the wild in order to continuously update and improve accuracy and speed

- Write and maintain scalable, performant and secure code that can be shared across platforms

- Meaningfully contribute to the product and core backend systems by suggesting and executing improvements

- Improve engineering standards, tooling, processes and security

- Develop novel, accurate, and performant ML algorithms for use at scale

- Conduct metric-driven research experiments to improve model performance

- Provide mentorship to and help onboard junior ML engineers

- Collaborate cross-functionally with other teams

- Utilize OWASP top 10 techniques to secure code from vulnerabilities

- Maintain awareness of industry best practices for data maintenance handling as it relates to your role

- Adhere to policies, guidelines and procedures pertaining to the protection of information assets

- Report actual or suspected security and/or policy violations/breaches to an appropriate authority