Senior ML/AI Engineer

Clera

$170K — $230K *
Information Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years of experience in Machine Learning roles
  • M.S. or Ph.D. in Computer Science, Machine Learning, or related field
  • Hands-on experience with Python and its ML ecosystem (e.g. PyTorch, TensorFlow, scikit-learn, LangChain)
  • Proven production experience building and deploying end-to-end ML systems
  • Experience building agentic AI harnesses for production use cases
  • Strong knowledge of classical ML, statistics, and time series analysis
  • Experience with graph databases and proficiency with SQL

Responsibilities

  • Design, build, and deploy ML models for demand forecasting and time series prediction
  • Develop the platform's agentic AI architecture to reason across heterogeneous data sources
  • Build and maintain ML pipelines for data preprocessing and model training
  • Architect RAG systems and LLM integrations for natural language interfaces
  • Collaborate with backend engineers to ensure production-grade models
  • Own model performance with continuous monitoring and improvement
  • Stay updated with AI research to innovate and enhance the platform

Benefits

  • Early-stage equity participation
  • Opportunity to shape the technical direction of a high-growth AI platform
Full Job Description
About the Role

Join a fast-growing, AI-native consumer intelligence platform serving the world's largest retailers and brands. This is a rare opportunity to work at the intersection of cutting-edge AI research and real enterprise deployment - building the intelligent systems at the very core of the product. The platform reasons, forecasts, and acts autonomously across complex enterprise data landscapes, delivering demand forecasting, consumer intelligence, competitive analysis, and autonomous decision-making at scale.

You'll be a senior technical owner on a small, high-caliber engineering team, with direct impact on product and architecture decisions.
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 the platform'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 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
What We're Looking For

Required
  • 5+ years of experience in Machine Learning roles, with significant experience designing and building ML systems architecture
  • M.S. or Ph.D. in Computer Science, Machine Learning, or a related field
  • Extensive hands-on experience with Python and its ML ecosystem (e.g. PyTorch, TensorFlow, scikit-learn, LangChain)
  • Proven production experience building and deploying end-to-end ML systems
  • Experience building agentic AI harnesses for production use cases
  • Strong foundational ML knowledge beyond LLM application (classical ML, statistics, time series, etc.)
  • Experience with graph databases
  • Proficiency with SQL

Nice to Have
  • Experience working in startup environments or at large-scale tech companies
  • Experience with Go

Visa sponsorship is not available for this role.
Location

This role is fully on-site in New York, NY (5 days/week in office). Candidates must be based in New York or willing to relocate. No visa sponsorship is available.
Compensation & Benefits
  • Base salary: $170,000 - $230,000 USD annually, depending on experience
  • Early-stage equity participation
  • Opportunity to shape the technical direction of a high-growth AI platform from the ground up

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