At Enable, we are transforming the supply chain with our cutting-edge rebate management software. We see rebates as a strategic advantage, strengthening partnerships, driving smarter decisions, and unlocking significant value across the entire supply chain – from manufacturers to consumers.
After securing $276M in Series A-D funding, we are positioned for continued, significant growth. Since the launch of our flagship product in 2016, we have been rapidly scaling our client base, product offerings, and built a team of top-tier talent committed to reshaping the industry.
Want a glimpse into life at Enable? Visit our Life at Enable page to learn how you can be part of our journey.
We’re hiring a Senior Machine Learning Engineer to join our AI and Architecture team, contributing to the design, development, and deployment of cutting-edge machine learning systems. In this role, you’ll work closely with ML scientists, data engineers, and product teams to help bring innovative solutions—such as retrieval-augmented generation (RAG) systems, multi-agent architectures, and AI agent workflows—into production.
As a Senior Machine Learning Engineer, you’ll play a key role in developing and integrating cutting-edge AI solutions—including LLMs and AI agents—into our products and operations at a leading SaaS company. You’ll collaborate closely with product and engineering teams to deliver innovative, high-impact systems that push the boundaries of AI in rebate management. This is a highly collaborative and fast-moving environment where your contributions will directly shape both the future of our platform and your own growth.

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 a 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 environments.
• 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 a related technical discipline.
• Experience with multi-agent RAG systems or AI agents coordinating workflows for advanced information retrieval.
• Familiarity with prompt engineering and building 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.

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