Senior ML/AI Engineer

Job Summary

We are seeking a Senior Machine Learning Engineer to design, build, and scale machine learning systems that turn complex data into reliable, production-ready solutions. In this role, you will apply deep expertise in TensorFlow to develop models, optimize performance, and support end-to-end ML workflows from experimentation through deployment and monitoring. You will partner closely with data scientists, software engineers, product teams, and stakeholders to translate business needs into practical machine learning applications that deliver measurable impact.

This position is ideal for an experienced engineer with 5+ years of hands-on machine learning experience who enjoys solving challenging technical problems, improving model quality, and building maintainable systems that can operate at scale. You will help shape best practices for model development, testing, reproducibility, and deployment while contributing to a collaborative engineering culture focused on quality, innovation, and continuous improvement. The role offers the opportunity to work on meaningful ML initiatives, mentor team members, influence technical direction, and help deliver intelligent products and services that create value for users and the organization.

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 company'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.

Requirements

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

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.

Bonus Skills

Experience with graph databases or graph RAG systems (major plus — core to 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 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.

Pay: $180,000.00 - $220,000.00 per year

Benefits:

Flexible schedule

Work Location: Hybrid remote in New York, NY