Reasonal AI

Founding Machine Learning Engineer

Posted 17 Days Ago

New York

150K-200K Annually

Junior

Artificial Intelligence • Fintech • Machine Learning

The Role

Lead design, implementation, and productionization of the platform's ML memory layer and LLM strategies. Build and operate training, fine-tuning, inference, monitoring, and deployment pipelines; collaborate cross-functionally to ensure scalable, reliable ML systems that improve with data and integrate deeply into product workflows.

Summary Generated by Built In

About Reasonal

We are building the world’s first email intelligence platform for finance, enabling investment banks to close M&A transactions twice as fast and win more deals. Our highly advanced systems materially alter billion-dollar transaction outcomes. We are especially lean, with every member being mission critical and deeply engrained with our investment bank clients.

Reasonal has gone from 0 to ~$1M in contracts in 4 months with some of the world’s leading global investment banks.

Our team works extremely hard to tackle novel engineering and research problems that require a diverse set of perspectives and skills. Join us if you want a meaningful, challenging role building the agentic operating system for financial enterprise. Expect a talent-dense team (e.g. former exited founders, PhDs, chess world record holders) operating in a high performance culture, in-person in NYC, with high equity ownership.

Role: Founding Machine Learning Engineer

What You Will Own:

As a Founding Machine Learning Engineer at Reasonal AI, you will lead the ML team to design, build, and scale the memory layer that powers our platform — from investor recommendation systems and ranking to graph-based information retrieval systems.You will productionize models, integrate them deeply into the product, and build robust pipelines to ensure our system improves continuously as data flows through the secure applications. You’ll work closely with the founders to turn Reasonal’s ML developments into a lasting competitive advantage.

Own end-to-end ML system lifecycles, from research prototyping through production deployment, monitoring, iteration, and long-term maintenance.
Lead the design and implementation of training, fine-tuning, post-training, and inference strategies for large language models.
Set technical direction and best practices for ML infrastructure, data pipelines, evaluation frameworks, and deployment workflows in a cloud environment.
Identify and resolve complex, ambiguous problems in model behavior, data quality, scaling, and system interactions, often before they surface as user-visible issues.

What You Will Need:

Deep, hands-on experience building, fine-tuning, and post-training large language models or other foundation models, including an understanding of failure modes and trade-offs.
Demonstrates strong command of modern ML research, with the ability to critically evaluate new papers and decide what is production-worthy versus experimental.
Have extensive experience deploying, monitoring, and operating ML systems in production, including model versioning, rollback strategies, and performance regression detection.
Highly effective at cross-functional collaboration, working end-to-end with product, infra, research, and data teams to deliver outcomes—not just models.

Illustrative Candidate Profile:

2+ years of experience, depending on education, as an MLE
1+ years of experience working on a cloud environment such as GCP, AWS, Azure, and with dev-ops tooling such as Kubernetes.
1+ years of experience designing and developing online and production grade ML systems
A degree in computer science, engineering, or a related field. PhD is a plus.

Bonus:

The ability to establish, manage, and use data and compute infrastructure on Azure, including tools such as Azure ML, Foundry, Bicep, MLFlow.
Deep understanding of knowledge graph architecture, and its applications for search, retrieval, and memory systems.
Experience building large scale recommendation systems.
Prior startup or early-stage experience (Seed - Series A).

Who You Are:

You are self-motivated and care a lot about what you do, and you're ecstatic to work at a startup.
You have fun solving problems and learning about AI, technology, and finance workflows.
You constantly push the boundaries of engineering possibilities.
You are autonomous, self-directed, and comfortable working with ambiguity.
You are collaborative, organized, thoughtful, and kind.

What we Offer: We invest heavily in employee wellbeing. We work especially hard and expect world class performance, and we reciprocate with world class benefits.