Senior Machine Learning Engineer

Skills

About the Role

You will own applied machine learning end to end, turning loosely defined problems into datasets, experiments, models, and production systems. You will build and operate production Python systems for data collection, enrichment, feature extraction, scoring, evaluation, and AI-assisted research. You will develop trustworthy models through careful labeling, feature design, evaluation, backtesting, leakage detection, calibration, interpretability, and model selection. You will move models into production by managing artifacts, schemas, APIs, background jobs, observability, failure handling, and releases. You will improve LLM systems through structured extraction, research agents, prompt and model evaluation, and safeguards for untrusted data. You will work directly with stakeholders to prioritize work, explain model behavior and tradeoffs, and improve systems based on usage.

Requirements

• Senior experience in applied machine learning and Python
• Ability to take ambiguous problems from experimentation through reliable production release
• Experience with data exploration, training code, application code, APIs, and production debugging
• Strong modeling judgment in problem definition, label definition, feature design, evaluation, backtesting, leakage detection, missing data, calibration, interpretability, and model selection
• Software and data engineering skills for building pipelines and services, integrating external APIs, managing model artifacts and schemas, and maintaining production workflows
• Practical experience with LLM systems, structured outputs, model and prompt evaluation, observability, retries, cost and latency tradeoffs, and safe handling of untrusted inputs
• Ability to communicate clearly with non-technical stakeholders and apply product judgment
• NYC-based or willing to relocate to NYC

Responsibilities

• Own applied machine learning end to end from problem definition through production
• Build and operate production Python systems for data collection, enrichment, feature extraction, scoring, evaluation, and AI-assisted research
• Define labels and features, build evaluation sets and backtests, detect leakage and bad source data, compare approaches, and select appropriate models
• Move model work into production by managing artifacts, feature and prompt compatibility, APIs, background jobs, observability, failure handling, and releases
• Improve LLM systems through structured extraction, research agents, prompt and model evaluation, and safety guardrails
• Work directly with stakeholders to prioritize what to build, explain model behavior and tradeoffs, and iterate based on system usage