Senior ML Engineer
MLabs · New York City · $300,000–$375,000 (stated in the posting)
Location: New York, United States (Office) - Must be able to work office based in NYC
On-Site | Full-time
Compensation: $300K - $375K
Our client, a premier venture accelerator backing high-growth technology and emerging-tech startups, is seeking an experienced and self-directed Senior Machine Learning Engineer to join their in-house engineering team in New York City.
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.
Key Responsibilities
- 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.
Requirements
Basic Qualifications
- 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).
Preferred Qualifications
- 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).
Benefits
- 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.
Interview Process
- Hiring Manager Interview
- Technical Interview
- Behavioral & Culture Interview (with the CPO & Co-Founder)
- Final Interview (Technical Coding Assessment)
Due to the high volume of applications we anticipate, we regret that we are unable to provide individual feedback to all candidates. If you do not hear back from us within 4 weeks of your application, please assume that you have not been successful on this occasion. We genuinely appreciate your interest and wish you the best in your job search.
Commitment to Equality and Accessibility:
At MLabs, we are committed to offer equal opportunities to all candidates. We ensure no discrimination, accessible job adverts, and providing information in accessible formats. Our goal is to foster a diverse, inclusive workplace with equal opportunities for all. If you need any reasonable adjustments during any part of the hiring process or you would like to see the job-advert in an accessible format please let us know at the earliest opportunity by emailing human-resources@mlabs.city.
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