About the Company

An applied AI and data analytics company building the intelligence layer for enterprise decision-making. The platform connects an organization's entire data landscape — internal systems, social media trends, industry reports, consumer behavior signals — into a single coherent intelligence layer that surfaces insights and automates workflows that used to take analysts weeks.

The core thesis: research and data are stuck in an outdated state — very little of it is connected, and massive value gets lost in that gap. The company is flipping sentiment from a lagging indicator into a leading indicator, helping brands make decisions months faster than legacy research tools allow. The platform is building toward a consumer intelligence ontology — powered by a production graph RAG system, connecting dots across temporal and sentiment data at a scale unlike anything built before.

The platform drives 8-figure improvements in gross margins for Fortune 500 retailers, with a land-and-expand strategy starting in insights/research departments and expanding into innovation, marketing, supply chain, and manufacturing.

Founded by a technical team with deep innovation and graph database backgrounds. Raised $14M in seed funding. Just launching publicly after nearly two years in stealth. Zero attrition — no one has left the company. Strong culture: weekly team activities, happy hours, game nights. This is a ground-floor opportunity — engineers joining now will have outsized influence on architecture, product direction, and culture.

11–50 employees | Founded 2024 | Validated with Fortune 500 brands during a 2-year stealth phase | Serves Fortune 100 brands

About the Role

You'll build and deploy the intelligent systems at the core of the platform. This is applied AI at its most impactful — not research for the sake of papers, but production systems that reason, forecast, and act 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.

The company is running experiments at the fringes of modern technology — ML, graph databases, agentic AI — and wants engineers who share the drive to stay at the forefront and turn tech innovation into real product value. Hands-on role: prototype to production, and keeping it running at scale.

Same cultural bar as all roles on the team: senior enough to think deeply, but still has boundless energy for implementation. High agency, low ego, great communicator.

What You'll Own

• 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 the agentic AI architecture — 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 and improve the production graph RAG system — a core technical differentiator
• Architect RAG systems and LLM integrations powering 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

What We're Looking For

Must Have

• 5+ years of experience in applied machine learning and AI, with models deployed and running in production
• M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or related field (or equivalent practical experience — what you've built matters more than the degree)
• Deep proficiency in Python with experience in ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Strong background in statistical analysis, predictive modeling, and time series forecasting
• Experience with applied agentic AI/ML systems and multi-agent orchestration
• Experience with NLP, LLMs, and RAG architectures
• Comfortable working with large-scale datasets and distributed computing environments

Nice to Have

• Graph databases or graph RAG systems experience — major plus, core to the company's stack
• Background in retail, supply chain, or demand forecasting domains
• Experience with graph neural networks or knowledge graphs
• Familiarity with MLOps platforms and model serving infrastructure
• Contributions to open-source ML/AI projects or published research

Ideal Background

Senior applied ML engineers from high-agency, innovation-driven companies working at the fringes of modern AI — verticalized AI intelligence layers, enterprise data companies, retail tech (demand forecasting/pricing), NLP-heavy product companies, or applied AI startups who've deployed models at scale. Big tech is acceptable only in newer product areas with real ownership — the signal is whether they've had to innovate and build, not just optimize existing models.