Senior Applied Machine Learning Engineer

What you'll do

• Develop, fine-tune, and deploy LLM and VLM-based systems for browser automation and buyer simulation.
• Move model improvements from experimental stages to production-ready systems like VLM teacher distillation and SFT rollouts.
• Design and build scalable ML components, data pipelines, and model-serving paths.
• Improve human-vs-agent alignment through rigorous offline and online evaluation practices.
• Contribute to golden datasets, trace replay systems, and benchmark infrastructure.
• Use large-scale behavioral and storefront data to improve personalization and merchant-facing recommendations.
• Diagnose complex model, data, and system failures with high attention to detail.
• Make practical technical tradeoffs across models, infrastructure, evaluation tooling, and data systems.

What we're looking for

• Strong applied ML engineering experience delivering production systems through ambiguous technical problems.
• Hands-on experience with LLM, VLM, or agent systems, including SFT, distillation, evaluation, and model serving.
• End-to-end experience training, evaluating, testing, deploying, and operating ML products at meaningful scale.
• Strong experimentation and evaluation instincts for designing metrics and diagnosing alignment failures.
• Experience building production ML data pipelines with large-scale behavioral, event, or product data.
• Proficiency with Python, shell scripting, orchestration tools, vector databases, and BigQuery/BigTable or equivalent systems.
• Experience with parallel or distributed ML environments, including GPU optimization and large-scale training/inference workflows.
• Strong software engineering fundamentals including code quality, operational awareness, and maintainability.
• Experience with browser automation, AI agents, or simulated user behavior (preferred).
• Background in e-commerce, search, recommendations, personalization, or buyer behavior modeling (preferred).
• Experience with sequence/behavior representation learning, vLLM/GPU serving, or Spark/GCS/Hugging Face-style data workflows (preferred).
• Prior experience translating research prototypes into reliable, production user-facing systems (preferred).