Machine Learning Engineer

Reporting directly to the Chief Technology Officer (CTO), the Senior Machine Learning Engineer will take full ownership of features throughout the product lifecycle-from requirements definition to production deployment. This onsite role requires an entrepreneurial mindset and deep technical execution to turn loosely defined problems into robust, production-ready machine learning and LLM-based systems without reliance on large engineering teams or dedicated project managers., * 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).

• Direct partnership with high-impact founders across emerging technology sectors.
• High-trust, high-autonomy environment within a lean, elite engineering team composed of industry veterans.
• Direct ownership over core systems shaping founder discovery and operational workflows.
• Accelerated career growth and networking opportunities within a leading startup ecosystem.
• Competitive compensation and benefits package.