Senior ML/AI Engineer

• 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.

• 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.

• 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.

1. Recruiter screen.
2. Intro call with a member of the leadership team (culture and background fit).
3. Technical screen (45–60 minutes) with a senior engineer — architecturally focused with ML depth, probing on background and hands-on ability.
4. 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.
5. Offer.

Compensation