Sr. Machine Learning Engineer

Enable; United States; Remote; USD 90,000 - 150,000; Full time

Job summary

Join a forward-thinking company as a Senior Machine Learning Engineer, where you'll design and deploy innovative machine learning systems. Collaborate with experts to develop cutting-edge AI solutions, including retrieval-augmented generation systems and multi-agent architectures. This dynamic role offers the opportunity to shape the future of a rapidly growing platform, ensuring your contributions have a direct impact. With a focus on real-time data processing and advanced algorithms, you'll be at the forefront of technology, driving value for clients while working in a supportive and collaborative environment. If you're passionate about AI and eager to tackle complex challenges, this position is perfect for you.

Qualifications

• 5+ years in machine learning engineering or applied AI.
• Strong foundation in supervised/unsupervised learning.
• Proven experience with RAG systems and LLM applications.

Responsibilities

• Design and deploy RAG systems for production use.
• Optimize model pipelines for latency and scalability.
• Collaborate with MLOps and data engineering teams.

Skills

Machine Learning Engineering

Python

RAG Systems

Data Science Fundamentals

Fine-tuning and Distillation

Communication Skills

Education

Bachelor's or Master's in Computer Science

PhD in Computer Science or related discipline

Tools

PyTorch

Hugging Face Transformers

TensorFlow

FAISS

Pinecone

Weaviate

Docker

Kubernetes

MLflow

Weights & Biases

Job description

Do you want to help design new ways of processing enterprise-scale data at speed, learn leading-edge technologies, work on complex big-data algorithms, shape processes into a growing engineering organization, all while helping to scale a Series D rocket ship to the next level?

Then welcome to Enable.

What is Enable:

Enable is the SaaS rebate management platform that drives trusted relationships between B2B trading partners. We create value for our customers by providing technology solutions to automatically detect and report rebate dues. Customers configure their deals, Enable ingests and processes all sales transactions, enabling them to find rebates owed that they might otherwise miss.

Our work involves major challenges: processing enormous amounts of data in very short timeframes, performing billions of calculations per customer, and storing data in enterprise-scale databases. We offer reporting, deal editing, and collaboration capabilities. As market leaders, we develop new solutions daily.

Founded in 2016 with our flagship product, we have raised $276m across Series A, B, C, and D funding rounds. We are continuously expanding our client base, product portfolio, and talented team.

We’re hiring a Senior Machine Learning Engineer to join our AI and Architecture team, focusing on designing, developing, and deploying cutting-edge machine learning systems. You’ll collaborate with ML scientists, data engineers, and product teams to bring innovative solutions—such as retrieval-augmented generation (RAG) systems, multi-agent architectures, and AI agent workflows—into production.

In this role, you’ll develop and integrate advanced AI solutions—including LLMs and AI agents—into our products and operations, shaping the future of our platform and your own growth. This environment is collaborative and fast-paced, with your contributions directly impacting our platform’s evolution.

Key Responsibilities

• Design, build, and deploy RAG systems, including multi-agent and AI agent architectures for production use cases.
• Contribute to model development, including fine-tuning, parameter-efficient training (e.g., LoRA, PEFT), and distillation.
• Build evaluation pipelines to benchmark LLM performance and monitor production accuracy and relevance.
• Work across the ML stack—from data preparation and model training to serving and observability—independently or with specialists.
• Optimize model pipelines for latency, scalability, and cost-efficiency, supporting real-time and batch inference.
• Collaborate with MLOps, DevOps, and data engineering teams for reliable deployment and system integration.
• Stay informed about current research and emerging tools in LLMs, generative AI, and autonomous agents, and evaluate their practical use.
• Participate in planning, design reviews, and documentation to ensure robust, maintainable systems.

Required Qualifications

• 5+ years of experience in machine learning engineering, applied AI, or related fields.
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering, or related technical discipline.
• Strong foundation in machine learning and data science fundamentals—including supervised/unsupervised learning, evaluation metrics, data preprocessing, and feature engineering.
• Proven experience building and deploying RAG systems and/or LLM-powered applications in production.
• Proficiency in Python and ML libraries such as PyTorch, Hugging Face Transformers, or TensorFlow.
• Experience with vector search tools (e.g., FAISS, Pinecone, Weaviate) and retrieval frameworks (e.g., LangChain, LlamaIndex).
• Hands-on experience with fine-tuning and distillation of large language models.
• Comfortable with cloud platforms (Azure preferred), CI/CD tools, and containerization (Docker, Kubernetes).
• Experience with monitoring and maintaining ML systems in production, using tools like MLflow, Weights & Biases, or similar.
• Strong communication skills and ability to work across disciplines with ML scientists, engineers, and stakeholders.

Preferred Qualifications

• PhD in Computer Science, Machine Learning, Engineering, or related discipline.
• Experience with multi-agent RAG systems or AI agents coordinating workflows for advanced information retrieval.
• Familiarity with prompt engineering and evaluation pipelines for generative models.
• Exposure to Snowflake or similar cloud data platforms.
• Broader data science experience, including forecasting, recommendation systems, or optimization models.
• Experience with streaming data pipelines, real-time inference, and distributed ML infrastructure.
• Contributions to open-source ML projects or research in applied AI/LLMs.
• Certifications in Azure, AWS, or GCP related to ML or data engineering.

Enable Global Inc provides equal employment opportunities (EEO) without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, genetic information, marital status, veteran status, or other protected categories. We comply with applicable laws across all our locations. We prohibit unlawful employee harassment and interference with job duties.