Senior ML/AI Engineer

**Merciv** Senior ML/AI Engineer; $180K – $225K + Equity (~25% of base/yr) + Bonus; New York City (On-site)

**About the Company**

Merciv is 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The core thesis: research and data are in an outdated state — very little data is connected to one another, and massive value gets lost in that dark data. Merciv is flipping sentiment from a lagging indicator into a leading indicator — enabling brands to make decisions months faster than legacy research tools allow. The platform is building toward a consumer ontology (think: Palantir's ontology, but for consumer intelligence) powered by a production graph RAG system, connecting dots across temporal and sentiment data at a scale unlike anything that's been built.

The platform drives 8-figure improvements in gross margins for Fortune 500 retailers. Land-and-expand strategy: starting in insights/research departments, expanding into innovation, marketing, and ultimately supply chain and manufacturing. Founded by a technical team with deep innovation and graph database backgrounds. Raised $14M in seed funding. Launching publicly after nearly two years in stealth. Zero attrition — no one has left the company. Strong culture: weekly team activities (ping pong tournaments, Yankees games, happy hours, game nights), plus ones welcome at events. This is a ground-floor opportunity — the engineers joining now will have outsized influence on architecture, product direction, and culture.

**About the Role**

As a Senior ML/AI Engineer, you'll build and deploy the intelligent systems at the core of Merciv's 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. This is a hands-on role: prototype to production, and keeping it running at scale. Same cultural bar as all roles: senior enough to think deeply, but still has boundless energy for implementation. High agency, low ego, great communicator.

**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 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 and improve the production graph RAG system — a core technical differentiator - Architect RAG systems and LLM integrations that power 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

**Required Skills**

A. 5+ years of experience in applied machine learning and AI, with models deployed and running in production environments

B. 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)

C. Deep proficiency in Python with experience in ML frameworks (PyTorch, TensorFlow, scikit-learn)

D. Strong background in statistical analysis, predictive modeling, and time series forecasting

E. Experience with applied agentic AI/ML systems and multi-agent orchestration

F. Experience with NLP, LLMs, and RAG architectures

G. Comfortable working with large-scale datasets and distributed computing environments

**Bonus Skills**

- Experience with graph databases or graph RAG systems (major plus — core to Merciv'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

**Logistical Info**

- Location: New York City - 4 days/week in office, engineering typically takes Fridays flexible/remote. Additional case-by-case flexibility available; in-person culture with understanding, not a hard-and-fast 5-day rule - Compensation: $180,000 – $225,000 base + equity (~25% of salary/yr, vesting) + bonus - Number of openings: Up to 2 (likely 1 senior + 1 mid-to-senior; strong candidates who aren't fully senior may be considered at a slightly lower level or funneled to full-stack) - **Other:** - Health, dental, vision, 401k - Home office stipend, flexible PTO - Ground-floor equity at a well-funded seed-stage company Strong team culture: weekly activities, team events, zero attrition to date

**Ideal Background** - Senior applied ML engineer from a high-agency, innovation-driven company working at the fringes of modern AI. - Target companies: Palantir (ontology/graph experience), Cognition, Harvey, Rogo, Cursor, and similar verticalized AI intelligence layers. - Also strong: ML engineers from enterprise data companies (Databricks, Snowflake ecosystem), retail tech (demand forecasting, pricing), NLP-heavy product companies, or applied AI startups who have deployed models at scale. - startup or small-team experience strongly preferred. Big tech okay only if on newer product areas with real ownership. The signal is whether they've had to innovate and build new things, not just optimize existing models.

**Green Flags**

- High agency, low ego — technically strong but humble, confident without being overconfident - Good communicator — articulate about their work, responsive, professional - Eager without being desperate — genuinely excited about applied AI, graph RAG, and the consumer ontology vision - Builds things on the side — personal projects, open source, research that they've taken to production - Has deployed ML models to production that serve real enterprise users — not just research prototypes - Experience with both classical ML (time series, forecasting) AND modern LLM/agentic architectures - Can talk about model monitoring, retraining, and production ML lifecycle - Excited about graph databases and novel approaches to data connectivity

**Red Flags**

- Shiny logos without depth — big co ML roles where scope was narrow and agency was low - Purely research-oriented — publishes papers but has never deployed models to production - Senior title but past the hands-on phase — wants to lead ML strategy but not implement - Overconfident / high ego - Poor communicator - Relies heavily on AI tools during technical interviews - No evidence of building full pipelines end-to-end — only worked on the model layer - Not willing to be in-office in NYC - Can't articulate how to monitor, debug, or retrain models in production

**Interview Process** - Recruiter screen - Intro call with Rachel or Bapt (culture + background fit) - Technical screen (45–60 min) with senior engineer — architecturally focused with ML depth, probing on background and hands-on ability - On-site (4 hours) — ML coding interview, System design interview (ML infrastructure focus), Product sense (30 min), AI sense (30 min), Meeting with Bapt + co-founder Shia. Note: decision often made after first 2 on-site interviews — most candidates don't make it through the full day - Offer