About Merciv

Merciv is building the intelligence layer for enterprise commerce. We connect an organization's entire data landscape — internal systems, social signals, industry reports, consumer behavior — and surface the insights and automated workflows that used to take analysts weeks.

We raised $14M in seed funding, spent two years in stealth building the right thing, and we're now launching publicly. Our platform already drives 8-figure gross margin improvements for some of the world's largest retailers.

We're a lean, high-ownership team. If you want to see your work matter immediately, this is it.

The Role

You'll build and deploy the intelligent systems at the core of Merciv. Our platform doesn't just surface insights — it reasons, forecasts, and acts autonomously across complex enterprise data landscapes. You'll develop the models and agentic architectures that power demand forecasting, consumer intelligence, competitive analysis, and autonomous decision-making for the world's largest retailers.

This is applied AI at its most impactful. You'll work at the intersection of cutting-edge research and real enterprise deployment, building systems that generate tens of millions in value for clients.

What You'll Do

Design, build, and deploy ML models for demand forecasting, time series prediction, consumer sentiment analysis, and anomaly detection at enterprise scale

Develop and iterate on Merciv's agentic AI architecture — building systems that reason across heterogeneous data sources and take autonomous action

Build and maintain robust ML pipelines: data preprocessing, feature engineering, model training, evaluation, and production deployment

Architect RAG systems and LLM integrations that power Merciv's natural language interfaces and autonomous workflows

Collaborate with backend engineers to ensure models are production-grade — optimized for latency, reliability, and scale

Own model performance end-to-end: monitoring, retraining, and continuous improvement in production

Stay at the frontier of AI research and bring relevant innovations into the platform

You May Be a Good Fit If

You have 5+ years of applied ML/AI experience with models deployed in production

You have an M.S. or Ph.D. in CS, Machine Learning, Statistics, or equivalent practical experience

You're deeply proficient in Python with hands-on experience in PyTorch, TensorFlow, or scikit-learn

You have a strong background in statistical analysis, predictive modeling, and time series forecasting

You've worked on agentic AI systems, multi-agent orchestration, NLP, LLMs, and RAG architectures

You care about model interpretability and building systems enterprise users can actually trust

Former technical founders are encouraged to apply — we care more about what you've built than how long you've been building

Bonus points for

Background in retail, supply chain, or demand forecasting

Experience with graph neural networks or knowledge graphs

Familiarity with MLOps platforms and model serving infrastructure

Open-source contributions or published research

How We're Different

Most AI platforms bolt intelligence onto existing workflows. We're building the intelligence layer from scratch — connecting every data source an enterprise touches and making it actionable in real time. Our agentic infrastructure doesn't just surface insights; it acts on them. That's a harder problem, and it's the one we're solving.

Why Merciv

Our platform drives 8-figure gross margin improvements for the world's largest retailers — you'll see real impact fast

Hard technical problems at the intersection of agentic AI, distributed systems, and enterprise data

Small team, massive surface area — you own what you build

Competitive salary, meaningful equity, health/dental/vision, 401k, home office stipend, flexible PTO

A culture built on craft, intellectual honesty, and real human connection

Interview Process

Application reviewed by a human

Intro call with our recruiting team

Technical conversation with an engineering lead

On-site — meet the team, work through a real problem