ML Engineer for Agentic Creator Studio

Socket.dev; Cary (NC); On-site; USD 120,000 - 170,000; Full time

Job summary

Socket.dev is building agentic intelligence inside Apple's professional creative apps, shaping how models complement creators. As an ML engineer, you'll work alongside designers, QA, and app teams to turn model capabilities into useful features that ship inside real products.

You will evaluate prompts, implement tool-use patterns, and optimize performance for on-device and resource-constrained environments, bringing strong Python and ML toolkit experience to a fast-paced, creative-focused team.

Qualifications

• 3+ years developing and shipping reliable, maintainable code
• Hands-on experience applying LLMs/foundation models to real products
• Strong intuition for experimental design and statistics to distinguish signal from noise
• Good product sense: choose experiences that matter to users
• Ability to collaborate with technical and non-technical partners
• Bachelor's degree in Computer Science or related field or equivalent experience

Responsibilities

• Work at the intersection of frontier models and professional creative apps
• Develop and ship features that turn model capabilities into usable product experiences
• Evaluate prompts, tools, and workflows to optimize user outcomes
• Collaborate with designers, QA, and app teams to ship reliable experiences
• Prototype and optimize model performance on resource-constrained / on-device platforms
• Contribute to on-device optimization using Core ML and related toolkits

Skills

Python programming

Experiment design

Communication with cross-functional

ML deployment

On-device optimization

Swift / iOS/macOS development

Education

Bachelor's degree in Computer Science or related field

Tools

PyTorch

JAX

TensorFlow

Core ML

Job description

Socket.dev is building agentic intelligence inside Apple's professional creative apps, shaping how models complement creators. As an ML engineer, you'll work alongside designers, QA, and app teams to turn model capabilities into useful features that ship inside real products.

You will evaluate prompts, implement tool-use patterns, and optimize performance for on-device and resource-constrained environments, bringing strong Python and ML toolkit experience to a fast-paced, creative-focused team.