Senior ML Engineer

• →Own Applied ML End-to-End: Translate ambiguous, high-level 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 optimal modeling approaches (including traditional ML and LLMs).
• →Production Deployment & Operations: Transition models from research to production environments, managing 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 iteratively based on real-world usage.

• Senior-Level ML Expertise: Proven ability to drive complex, ambiguous ML problems from initial experimentation through to reliable production releases.
• Production Python Proficiency: Strong mastery of Python across data exploration, training pipelines, application logic, APIs, and production debugging.
• Robust Modeling Judgment: Deep experience in problem formulation, label definition, feature engineering, evaluation metrics, backtesting, data leakage prevention, calibration, interpretability, and model selection.
• Full-Stack ML Engineering Capability: Strong software engineering and data pipeline fundamentals to deploy, integrate, schema-manage, and monitor services autonomously.
• Applied LLM Systems Experience: Hands-on experience with structured outputs, model/prompt evaluation frameworks, observability, retry strategies, cost/latency optimization, and input validation.
• Product Sense & Communication: Ability to communicate technical tradeoffs clearly with non-technical stakeholders and translate model outputs into actionable business tools.
• Location: Based in or willing to relocate to New York City (onsite presence is strictly required).

• Track record of shipping customer-facing ML products with end-to-end ownership.
• Strong portfolio of technical work (e.g., active GitHub, open-source contributions, published research, or technical writing).
• Prior experience developing prediction, ranking, classification, recommendation, or anomaly-detection systems on messy, real-world data.
• Background building LLM evaluation frameworks, structured extraction pipelines, or automated research agents.
• Early-stage startup experience as a founder, early ML hire, or senior IC working without dedicated platform teams.
• Exceptional technical or quantitative pedigree (e.g., strong research background, competition achievements, or top-tier academic background in quantitative disciplines).

1. Hiring Manager Interview
2. Technical Interview
3. Behavioral & Culture Interview (with the CPO & Co-Founder)
4. Final Interview (Technical Coding Assessment)

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