Job Description
About Our Client
Our client is an applied AI and data analytics company building the intelligence layer for enterprise decision-making. Their platform unifies an organization’s entire data landscape — internal systems, social media signals, industry reports, and consumer behavior data — into a single coherent intelligence layer that surfaces insights and automates workflows that historically took analysts weeks to complete.
The guiding thesis: research and data today exist in a fragmented state, with very little of it connected, and enormous value lost in that “dark data.” Our client is converting sentiment from a lagging indicator into a leading one — enabling brands to make decisions months earlier than legacy research tools allow. The platform is being built toward a consumer ontology (conceptually similar to Palantir’s ontology, but applied to consumer intelligence), powered by a production graph RAG system that connects signals across temporal and sentiment data at a scale that hasn’t been attempted before.
The platform has driven 8-figure gross margin improvements for Fortune 500 retailers. The go-to-market motion is land-and-expand: beginning in insights and research teams, then growing into innovation, marketing, and ultimately supply chain and manufacturing.
Founded by a technical team with deep backgrounds in innovation and graph databases. The company has raised $14M in seed funding and is launching publicly after nearly two years operating in stealth. Attrition is zero — no one has left the team. Culture is a real point of pride: weekly team activities (ping pong tournaments, Yankees games, happy hours, game nights), and plus-ones are welcome at events.
This is a true ground-floor opportunity — engineers joining at this stage will have outsized influence on architecture, product direction, and culture.
About the Role
As a Senior ML/AI Engineer, you will design and ship the intelligent systems that sit at the core of the platform. This is applied AI at its most consequential — not research aimed at publishing papers, but production systems that reason, forecast, and act autonomously across complex enterprise data environments. You will build the models and agentic architectures behind demand forecasting, consumer intelligence, competitive analysis, and autonomous decision-making.
The team is running experiments at the frontier of modern technology — ML, graph databases, and agentic AI — and is looking for engineers who share the drive to stay on that edge and translate technical innovation into real product value. The role is hands-on from prototype through production, including keeping systems running reliably at scale.
Same bar as every other role on the team: senior enough to think deeply, but with the energy to roll up sleeves and execute. High agency, low ego, and a strong communicator.
Key Responsibilities
- 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 company’s agentic AI architecture — systems that reason across heterogeneous data sources and take autonomous action.
- Build and maintain robust ML pipelines spanning data preprocessing, feature engineering, model training, evaluation, and production deployment.
- Architect and continuously improve the production graph RAG system, which is a core technical differentiator for the platform.
- Design RAG systems and LLM integrations that power natural language interfaces and autonomous workflows.
- Partner with backend engineers to ensure models are production-grade — optimized for latency, reliability, and scale.
- Own model performance end-to-end, including monitoring, retraining, and ongoing improvement in production.
- Stay current on AI research and bring relevant advances into the platform.
Requirements
- 5+ years of experience in applied machine learning and AI, with models deployed and operating in production.
- M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field — or equivalent practical experience (what you’ve built matters more than the degree).
- Deep proficiency in Python, with hands-on experience across ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
- Strong foundation in statistical analysis, predictive modeling, and time series forecasting.
- Experience building applied agentic AI/ML systems and orchestrating multiple agents.
- Experience with NLP, LLMs, and RAG architectures.
- Comfort working with large-scale datasets and distributed computing environments.
Bonus Skills
- Experience with graph databases or graph RAG systems (a 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.
- Open-source contributions to ML/AI projects, or published research.
Logistics
Location
New York City — 4 days/week in office. Engineering typically has Fridays flexible or remote. Additional flexibility is considered case-by-case; the team values in-person culture but treats it as a norm rather than a hard 5-day rule.
Compensation
$180,000 – $225,000 base salary, plus equity (approximately 25% of salary per year, vesting) and bonus.
Benefits & Other
Health, dental, vision, and 401(k).
Home office stipend and flexible PTO.
Ground-floor equity at a well-funded seed-stage company.
Strong team culture: weekly activities, team events, and zero attrition to date.
Interview Process
- Recruiter screen.
- Intro call with a member of the leadership team (culture and background fit).
- Technical screen (45–60 minutes) with a senior engineer — architecturally focused with ML depth, probing on background and hands-on ability.
- On-site (approximately 4 hours), covering: an ML coding interview, a system design interview focused on ML infrastructure, a product sense session (30 minutes), an AI sense session (30 minutes), and a meeting with leadership and a co-founder. Note: decisions are often made after the first two on-site interviews — most candidates do not advance through the full day.
- Offer.
