Machine Learning Scientist III – Growth Marketing (Production GenAI & Agentic Multimodal Workflows)

Responsibilities

  • Develop and deploy complex agentic and multimodal AI workflows at production scale, architecting memory-enabled, context-aware agents that drive dynamic automation, personalization, real-time monitoring and content generation.
  • Design and orchestrate advanced LLM and RAG-driven agent solutions using modular frameworks including LangGraph, Langfuse, CrewAI, AutoGen, and other emerging agentic orchestration tools—enabling adaptive, stateful, and highly interactive workflow graphs.
  • Integrate and optimize complete multimodal GenAI pipelines: text-to-image, image-to-video, text-to-video, and text-to-voice, leveraging and extending in-house and third party models.
  • Fine-tune (parameter-efficient tuning, LoRA, QLoRA, etc.), and evaluate (perplexity, accuracy, creativity metrics) both open-source (Llama, MPT, Phi-4, etc.) and commercial LLMs (OpenAI GPT-4, Gemini, Claude).
  • Pioneer LLM-based simulation & evaluation environments (e.g., LLM-as-a-Judge): Leverage the latest LLMs, multi-agent systems, and generative tools to simulate user behavior, accelerate experimental cycles, and rigorously evaluate new features or workflows in both synthetic and prod online shopping and travel environments.
  • Simulate A/B testing via LLM simulations: Build A/B test agents capable of simulating user engagement, behavioral outcomes, and marketing scenarios to complement and replace large-scale online testing—enabling rapid iteration, robust pre-deployment validation, and deeper insight into agentic/LLM-driven feature impact.
  • Continuously adopt and iterate on the latest frameworks, staying current with state-of-the-art in agentic orchestration, memory models, multimodal GenAI, LLM pipelines, and LLM operation (RAG, memory, tracing, evaluation).
  • Collaborate deeply with engineering, marketing, and product teams to translate evolving business objectives into scalable and observable AI-driven solutions.

Requirements

  • Minimum 7+ years experience delivering robust, scalable AI/GenAI solutions at enterprise scale with a Bachelors degree (4-5+ year of relevant experience with a Masters/PhD in CS, EE, Stats)
  • Agentic frameworks experience working with platforms such as LangGraph, Langfuse, RAG, CrewAI, AutoGen, and comparable agent workflow frameworks
  • Ability to design complex agent graphs with memory, tool calling, and multi-step reasoning
  • Multimodal and agentic AI experience in both commercial and open-source models (VLMs, CLIP, Phi-4, DINO, etc), advanced LLM pipeline design, and shipping both inference and generative AI features to users
  • Hands-on experience in fundamental neural network models (CNN, Transformers, BERT, VAEs, Multimodals etc)
  • Proven expertise in architecting persistent, hybrid, and context-managed memory for agentic workflows
  • Strong in Python (PyTorch, HuggingFace, etc.), MLOps, distributed systems (PySpark, Databricks, Airflow), and scalable API engineering
  • Deep experience with tracing, logging, and observability stacks (Langfuse, PromptLayer, Weights & Biases, etc.), including prompt monitoring and model evaluation in live production
  • Ability to translate complex technical systems into actionable solutions for cross-functional teams, leveraging Multimodal LLMs and Gen AI models
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