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TIFIN · 6 months ago

Staff ML Engineer, Applied AI

United States

Full-time

Remote

Senior Level, Lead/Staff

$190K/yr - $225K/yr

5+ years exp

TIFIN is an AI platform that develops fintech solutions focused on tailoring investment experiences to individual needs. As a Staff ML Engineer on the Applied AI team, you will own and lead projects across various generative AI product lines, focusing on building and optimizing AI models and workflows.

Artificial Intelligence (AI)Financial ServicesFinTech

Responsibilities

Design and fine-tune open source and proprietary LLMs for various tasks such as answering questions, summarization, reasoning and planning, etc

Build advanced Retrieval Augmented Generation (RAG) pipeline including rewriting, embedding fine-tuning, hybrid search, reranking, knowledge graphs, etc

Develop and integrate agentic workflow systems that empower AI agents to operate autonomously, enabling proactive decision-making and dynamic interactions

Apply reinforcement learning techniques—including reinforcement learning fine tuning (e.g., PPO, DPO, GRPO)—to continuously optimize model performance

Implement a comprehensive evaluation framework and metrics for model performance

Deploy models into production environments and ensure low latency, reliability, and scalability

Collaborate with product team and software engineering team to build end-to-end product systems

Qualification

Large Language Models (LLM)Natural Language Processing (NLP)Generative AIMachine LearningDeep LearningPythonSQLTensorFlowPyTorchLLM fine-tuning techniquesLLM inference frameworksRetrieval Augmented Generation (RAG)Data visualizationLORAVLLMLLM evaluation methodsKnowledge graphsHybrid searchRerankingPublications in ML conferences

Required

5+ years of experience (post-BS/MS) in applied AI/ML engineering

Experience shipping generative AI products powered by LLMs: designing and implementing agentic workflows that enable autonomous AI behaviors, leveraging the latest state-of-the-art research

Hands-on experience in various LLM fine-tuning techniques (e.g. LORA), LLM inference frameworks (e.g. vLLM), advanced RAG pipelines

Solid expertise in reinforcement learning fine tuning methodologies and frameworks to optimize AI models

Resilience and adaptability