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# Senior AI Machine Learning Engineer

- **Company:** Skills, Inc.
- **Location:** Boston, MA, United States
- **Experience:** Expert
- **Salary:** $166,400 - $174,720
- **Contract:** Temporary to permanent
- **Skills:** Artificial Intelligence, Airflow, Automated Storage and Retrieval Systems, Batch Processing, Cursor, Email Filtering, Python (Programming Language), Machine Learning, Metadata, Natural Language Processing, Large Language Models, Claude Code, Prompt Engineering, Deep Learning, Generative AI, Machine Learning Operations, Code Restructuring, Natural Language Understanding, Data Pipelines
- **Published:** November 12, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/88845178/1

## About the Role

* You have solid, hands-on experience developing and deploying machine learning systems in high-throughput production environments.
 * You have a demonstrated track record of owning ML solutions end-to-end-from initial problem formulation and data orchestration to shipping and monitoring models in production.
 * You bring experience with content classification, quality scoring, ranking signals, or abuse/spam detection across text, audio, video, or metadata.
 * You have deep experience with machine learning, deep learning, and modern AI technologies, with a pragmatic bias toward engineering execution and shipping impact.
 * You have hands-on experience working with large language models, NLP, retrieval-augmented generation (RAG), vector databases, or multimodal ML systems, and know how to adapt them for real-world product challenges.
 * You are comfortable diving into early-stage prototypes or existing codebases, making sound technical decisions on whether to iterate, optimize, or rewrite components from scratch.
 * You have practical experience with ML pipeline orchestration and tooling (e.g., Python, Ray, Flyte) to independently construct, train, and maintain ML workflows.
 * You have experience building robust evaluation frameworks and can quantify model performance through disciplined experimentation and measurement techniques.
 * You know how to navigate ambiguity and make thoughtful technical trade-offs that balance product impact, scalability, and engineering quality.
 * You are curious about emerging AI technologies and excited about integrating tools such as Claude Code, Cursor, and other AI-assisted capabilities into engineering workflows.

**About Skill:**

Skill connects the best professional, IT, engineering, financial and administrative talent with the world's biggest brands. Our eligible talent get access to benefits such as health benefit contributions, retirement plans with match and flexible spending accounts.

## Description

Working at the intersection of large-scale machine learning, pipeline engineering, and product innovation, the squad partners closely with Product, Engineering, and Data Science teams to build intelligent systems that improve how users experience content across the platform. In this role, you will play a central part in taking models end-to-end-translating ambiguous content challenges into robust, production-grade quality signals.
What You'll Do

 * Work with other senior and staff engineers on machine learning initiatives from ideation, problem formulation, and prototyping through experimentation, deployment, and large-scale productionization.
 * Design, develop, and deploy machine learning systems and quality signals that operate across hundreds of millions of content items using both real-time and batch processing architectures.
 * Advance our clients capabilities in natural language understanding, multimodal AI, and content intelligence, with a focus on quality classification, ranking signals, and spam detection.
 * Build, evaluate, and iterate on ML and LLM-powered solutions using modern prompting techniques, retrieval systems, and advanced model orchestration approaches.
 * Audit, refactor, and mature existing early-stage models and pipeline foundations, optimizing them for production reliability, throughput, and scale.
 * Define rigorous evaluation methodologies utilizing golden datasets, precision/recall frameworks, offline benchmarking, and online experimentation.
 * Partner closely with Product Managers, Engineering Managers, Staff Engineers, and Data Scientists to influence technical strategy, roadmaps, and architectural trade-offs.