Forward Deployed Engineer - Architect
DevRev- Full Time
- Junior (1 to 2 years)
Candidates should possess a Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or a related field. A minimum of 1 year of professional work experience in a fast-paced, high-growth environment is required. Demonstrated experience with one or more general-purpose programming languages in a production-level environment is essential, with a strong preference for Python. Familiarity with AI/ML pipelines and the lifecycle of ML model development and deployment is necessary. Strong communication skills, particularly regarding complex technical topics, are important, and experience in building or optimizing AI/ML projects is highly valued.
As a Forward-Deployed Engineer, you will develop and maintain software systems using general-purpose programming languages, primarily Python. You will collaborate with cross-functional teams to implement AI/ML pipelines, communicate complex technical topics clearly, and optimize AI/ML projects. You will own products and projects end-to-end, acting as both an engineer and a project manager while navigating ambiguity and making informed tradeoff decisions. Additionally, you will demonstrate pride, ownership, and accountability for your work and expect the same from your teammates.
Platform for deploying and managing ML models
Baseten provides a platform for deploying and managing machine learning (ML) models, aimed at simplifying the process for businesses. Users can select from a library of open-source foundation models and deploy them with just two clicks, making it easier to implement ML solutions. The platform features autoscaling, which adjusts resources based on demand, and comprehensive monitoring tools for tracking performance and troubleshooting. A key differentiator is Baseten's open-source model packaging framework, Truss, which allows users to package and deploy custom models easily. The company operates on a usage-based pricing model, where clients pay only for the time their models are actively deployed, helping them manage costs effectively.