Own Applied ML End-to-End: translate ambiguous business problems into datasets, experiments, models, and production systems independently.
Build Production Pipelines: develop and maintain production Python systems for data collection, enrichment, feature extraction, scoring, model evaluation, and AI-assisted research workflows.
Model Design & Evaluation: define labels and features, construct evaluation sets and backtests, detect data leakage, evaluate source quality, and select modeling approaches (including traditional ML and LLMs).
Production Deployment & Operations: transition models from research to production environments, manage artifacts, feature/prompt compatibility, APIs, background jobs, observability, failure handling, and release cycles.
Enhance LLM Infrastructure: improve large language model systems, including structured data extraction, research agents, prompt and model evaluation, and safety guardrails for untrusted external inputs.
Stakeholder Collaboration: work directly with key stakeholders to determine roadmap priorities, clearly articulate model behavior and tradeoffs, and iterate based on real-world usage.
Senior-Level ML Expertise: proven ability to drive complex, ambiguous ML problems from initial experimentation through to re

About the role

Senior Machine Learning Engineer is needed for MLabs Ltd in New York City. This is an on-site (office-based) role based in NYC.

Compensation: $300K - $375K. Full-time, on-site.

Basic Qualifications:

Own Applied ML End-to-End: translate ambiguous business problems into datasets, experiments, models, and production systems independently.
Build Production Pipelines: develop and maintain production Python systems for data collection, enrichment, feature extraction, scoring, model evaluation, and AI-assisted research workflows.
Model Design & Evaluation: define labels and features, construct evaluation sets and backtests, detect data leakage, evaluate source quality, and select modeling approaches (including traditional ML and LLMs).
Production Deployment & Operations: transition models from research to production environments, manage artifacts, feature/prompt compatibility, APIs, background jobs, observability, failure handling, and release cycles.
Enhance LLM Infrastructure: improve large language model systems, including structured data extraction, research agents, prompt and model evaluation, and safety guardrails for untrusted external inputs.
Stakeholder Collaboration: work directly with key stakeholders to determine roadmap priorities, clearly articulate model behavior and tradeoffs, and iterate based on real-world usage.
Senior-Level ML Expertise: proven ability to drive complex, ambiguous ML problems from initial experimentation through to re

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