Staff ML Engineer, Applied AI

Tifin•Boulder, CO

3d•$190,000 - $225,000

About The Position

TIFIN builds AI-powered financial technology that personalizes and improves financial advice across consumers, advisors, workplaces, and institutions. Our modular platform embeds finance-tuned AI to deliver dynamic, tailored guidance at scale—without added complexity. Combining proprietary models, specialized data, and a fast-paced engineering culture, we create secure, compliant tools that power real outcomes. Other differentiators include: Speed: Our ability to stand up businesses at 2-4x the speed of typical fintech companies (building MVPs in 3 months and production-ready products in 6-12 months) Track Record: Previous exits include 55ip (acquired by J.P. Morgan) and Paralel Strategic Partners: Partners include J.P. Morgan, Franklin Templeton, Morningstar, Broadridge, Hamilton Lane, Motive Partners and SEI. World-Class Team: Complimentary financial services & technical expertise from Google, Microsoft, Uber, PayPal, eBay, Techstars, BlackRock, LPL, Franklin Templeton, Morgan Stanley, Broadridge and more. OUR VALUES: Go with your GUT Grow at the Edge. We are driven by personal growth fueled by a beginner’s mindset. We get out of our comfort zone and keep egos aside. With self-awareness and integrity we strive to be the best we can possibly be. No excuses. Understanding through Listening and Speaking the Truth. We communicate with authenticity, precision and integrity to create a shared understanding. We identify opportunities within constraints and propose solutions in service to the team. I Win for Teamwin. We believe in staying within our genius zones to succeed and taking accountability for driving results. We are all individual contributors first and always thinking about what can be better. ROLE OVERVIEW As a Staff ML Engineer on the Applied AI team, you will own and lead projects across our various generative AI product lines. We are looking for individuals who have worked end-to-end and have experience building generative AI products where LLMs are a core component. Expertise with fine-tuning and reinforcement learning is critical. This is an individual contributor and technical leadership role reporting into the Head of AI. PROJECTS AI Agent Orchestrator: Multi-agent systems that automate tasks and streamline workflows, driving measurable improvements in operational efficiency. Human-in-the-Loop Collaboration: AI-powered co-pilot for advisors and other user personas, streamlining workflows across business workflows such as prospecting, conversion, onboarding, and client servicing. Fine-Tuned Models: Purpose-tuned models for financial services to handle complex, multi-turn interactions with actionability in low-latency environments. Seamless Legacy Tech Integration: Designed to navigate and optimize legacy systems for streamlined operations with computer use models.

Requirements

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

Nice To Haves

Resilience and adaptability - experience working at early-stage startups is a plus

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