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Senior ML/AI Engineer

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Onsite

Senior ML/AI Engineer

CrossTrove Talent Access

Senior ML/AI Engineer

Skills

PythonMachine LearningAI

1 month ago

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Important information


Contract type:

Permanent

Location:

New York, NY, USA

Work mode:

Onsite

Published on:

16 September 2026

What they need


Location: New York City, NY

Workplace: Hybrid — 4 days per week in office; Fridays typically flexible or remote

Employment Type: Full-time

Compensation: $180,000–$225,000 base + equity + bonus

Benefits

  • Health, dental and vision coverage
  • 401(k)
  • Home office stipend
  • Flexible PTO
  • Ground-floor equity

About the Role

You will build and deploy the intelligent systems at the core of the platform. This is applied AI focused on production systems that reason, forecast, and act autonomously across complex enterprise data — not research for the sake of papers.

The team runs experiments at the fringes of machine learning, graph databases and agentic AI. This is a hands-on role spanning prototype to production, including keeping systems running at scale.

What You'll Do

  • 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 agentic AI architecture, building systems that reason across heterogeneous data sources and take autonomous action.
  • Build and maintain robust ML pipelines covering preprocessing, feature engineering, 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.
  • Own model performance end to end, including monitoring, retraining and continuous improvement in production.

What You'll Need

  • 5+ years of experience in applied machine learning and AI, with models deployed and running in production.
  • Strong Python skills.
  • Experience with both classical ML, including time series and forecasting, and modern LLM and agentic AI architectures.
  • Full ML pipeline ownership, not just the model layer. You can speak concretely about monitoring, debugging and retraining models in production.
  • Startup or small-team experience, with real ownership rather than task-taking.
  • High agency, low ego and strong communication skills.

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