> Markdown version of [/jobs/ext/2652778-senior-ml-engineer](https://www.wearedevelopers.com/jobs/ext/2652778-senior-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior ML Engineer - **Company:** Mlabs Ltd - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $30,000.0 - $37,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Business Logic, Data Validation, Information Leak Prevention, Data Mining, Software Debugging, Github, Python (Programming Language), Machine Learning, Backtesting, Software Deployment, Software Engineering, Software Requirements Analysis, Feature Engineering, Large Language Models, Model Validation, Free and Open-Source Software, Feature Extraction, Data Pipelines - **Published:** August 3, 2026 - **Apply:** https://www.careerjet.com/jobad/usc12fdc7ec4d0d4625609a2b5293354bd ## About the Role * 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). ## Description 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. ## Related Videos - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [How to implement convenient Python bindings to C++](https://www.wearedevelopers.com/videos/618-how-to-implement-convenient-python-bindings-to-c) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) - [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Why Upskilling And Reskilling is Important For Developers](https://www.wearedevelopers.com/magazine/428-why-upskilling-and-reskilling-is-important-for-developers) - [7 Most Popular Web Developer Jobs in Europe](https://www.wearedevelopers.com/magazine/163-7-most-popular-web-developer-jobs-in-europe)