Applied Machine Learning Engineer

🚀 Hiring | Member of Technical Staff – Applied Machine Learning (AI Agents | LLMs | Production AI)

📍 New York City (On-site 5Days/week)

💰 $175K+Base with Competitive Equity

🛂 Visa Sponsorship Available

🏢 Full-Time

The next wave of AI won't be defined by better chatbots—it will be defined by autonomous AI systems that perform real work.

We're hiring Applied Machine Learning Engineers who want to build production-grade AI agents capable of solving complex accounting and finance problems at scale.

This is not a research role.

This is an opportunity to take cutting-edge AI research and transform it into reliable, scalable products used by real customers every day. You'll build intelligent systems that reason, plan, retrieve information, evaluate themselves, and continuously improve—all while operating in production environments where performance, reliability, latency, and accuracy matter.

Why This Role Is Different

Many ML roles focus on model training or publishing research.

This role is about shipping AI systems that solve real business problems.

You'll own problems end-to-end—from identifying opportunities and designing system architecture to experimentation, deployment, evaluation, and continuous optimization.

You'll think like a researcher, build like a software engineer, and deliver like a product owner.

You'll have the autonomy to:

• Design technical solutions from first principles
• Define success metrics
• Build production-ready AI infrastructure
• Run experiments at scale
• Make architectural decisions
• Own systems from idea to deployment.

What You'll Be Building

You'll help create the intelligence layer powering next-generation AI agents capable of handling sophisticated accounting workflows.

Your work will include:

🤖 Designing multi-agent AI systems that automate complex financial processes

🧠 Building reasoning pipelines that allow AI agents to plan, make decisions, and collaborate

📚 Architecting retrieval systems and contextual memory that improve agent performance

📊 Developing evaluation frameworks to benchmark models, measure quality, detect regressions, and continuously improve performance

⚡ Optimizing inference, latency, cost, accuracy, and reliability in production

🛡️ Building validation layers, guardrails, and safety mechanisms for dependable AI behavior

🔄 Running structured experiments that drive measurable product improvements rather than relying on intuition

Every feature you build will directly impact production AI systems used in real-world accounting workflows.

Who We're Looking For

We're looking for engineers who enjoy solving difficult engineering problems—not just training models.

Ideal candidates have:

✔ 4–12 years of Machine Learning experience

✔ Strong Python expertise

✔ Hands-on experience building LLM-powered applications

✔ Experience with AI Agents, prompt engineering, retrieval systems, model orchestration, evaluation frameworks, benchmarking, and production ML systems

✔ Experience designing experiments, measuring outcomes, and iterating using data

✔ Background in high-growth startups, AI-native companies, leading technology firms, or similarly fast-paced engineering environments

Most importantly, we're looking for builders who enjoy taking ownership and delivering production-ready AI systems that create measurable business impact.

Tech Stack

• Python

• PostgreSQL

• Large Language Models (LLMs)

• AI Agents

• Prompt Engineering

• Retrieval-Augmented Generation (RAG)

• Model Evaluation & Benchmarking

• Production ML Infrastructure

Why Join?

You'll be joining one of the fastest-growing AI startups working at the intersection of artificial intelligence and finance.

✅ Build AI products solving real-world challenges

✅ Work alongside exceptional engineers and founders

✅ Own high-impact technical initiatives

✅ Competitive salary ($175K–$350K)

✅ Meaningful equity

✅ Visa sponsorship available

✅ Opportunity to shape the future of Applied AI in accounting and finance

If you're excited about taking AI beyond demos and into production, this role offers the chance to build systems that will redefine how knowledge work gets done.

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