Machine Learning Engineer, Infrastructure - Attentive

Job Title: Machine Learning Engineer, Infrastructure
Job Location: San Francisco, CA
Job Listing URL: https://jobs.lever.co/attentive/d7a76898-d9b9-488e-a5ed-dd28e90a26d7
Job Description: Attentive® is the AI-powered mobile marketing platform transforming the way brands personalize consumer engagement. Attentive enables marketers to craft tailored journeys for every subscriber, driving higher recurring revenue and maximizing campaign performance. Activating real-time data from multiple channels and advanced AI, the platform personalizes content, tone, and timing to deliver 1:1 messages that truly resonate.

With a top-rated customer success team recognized on G2, Attentive partners with marketers to provide strategic guidance and optimize SMS and email campaigns. Trusted by leading global brands like Neiman Marcus, Samsung, Wayfair, and Dyson, Attentive ensures enterprise-grade compliance and deliverability, supporting trillions of interactions across more than 70 industries. To learn more or request a demo, visit www.attentive.com or follow us on LinkedIn, X (formerly Twitter), or Instagram.

Attentive’s growth has been recognized by Deloitte’s Fast 500, Linkedin’s Top Startups and Forbes Cloud 100 all thanks to the hard work from our global employees!

- Develop and implement Machine Learning models including design, development and deployment of machine learning models to address various business challenges.
- Train, evaluate and optimize models to ensure high performance.
- Build and maintain Machine Learning Infrastructure including develop infrastructure for machine learning model deployment.
- Implement and manage scalable machine learning systems on cloud platforms including AWS/Kubernetes Data Analysis and Feature Engineering.
- Perform data exploration and analysis to identify relevant features and prepare datasets for model training.
- Query, clean, and manipulate data with SQL and Pandas Monitor and Maintain Production Models.
- Monitor the performance of deployed models by logging using tools including Datadog and Monte Carlo.
- Work closely with product managers, software engineers and data scientists to deliver end-to-end machine learning solutions.
- Maintain eﬀective communication with other teams to ensure alignment and integration of machine learning models within a larger product ecosystem.
- Perform coding and documentation including writing clean scalable and documented code for machine learning models and related software.
- Maintain comprehensive documentation of relevant processes, results and systems.
- Keep a keen eye to recent advancements in research that may be relevant to workstreams.
- Maintain compliance and ethical standardization by ensuring models comply with data privacy and ethical standards.
- Telecommuting permitted 2 days per week.
- When not telecommuting, must report to 114 Sansome Street, 11th Floor, San Francisco, CA 94104.
MINIMUM REQUIREMENTS:
Bachelor’s degree or U.S. equivalent in Computer Science, Data Science, Computer Engineering, Mathematics, Statistics, or a related field plus 3 years of professional experience as a Software Engineer, Machine Learning Engineer, Data Scientist, or any occupation/position/job title involving machine learning and data analysis.
Must also have experience with the following special skills:
3 years of professional experience with machine learning frameworks and libraries including Pytorch, Tensorflow, Pandas, Numpy, and Sci-kit learn
3 years of professional experience utilizing Python and statistical models
3 years of professional experience working with cross-functional teams to build scalable systems and data-driven products
3 years of professional experience developing complex algorithms and troubleshooting issues to create robust and scalable machine learning systems
2 years of professional experience deploying and managing machine learning models in production environments with Kubernetes and AWS EKS
2 years of professional experience deploying advanced machine learning models into production, evaluating their performance, and running AB tests to verify their eﬃcacy