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.
Key Responsibilities
- 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.
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.
Total Rewards:
At Enable, we’re committed to helping all Enablees grow. During the interview process, we assess your level based on experience, expertise, and role scope, aligning it with our compensation bands. Starting pay is determined by factors like location, skills, experience, market conditions, and internal parity.
Salary/TCC is just one component of Enable’s total rewards package. Enable is committed to investing in the holistic health and wellbeing of all Enablees and their families. Our benefits and perks include, but are not limited to:
Paid Time Off: Ample days off + 8 bank holidays
Wellness Benefit: Quarterly incentive dedicated to improving your health and well-being
Private Health Insurance: Health and life coverage for you and your family
Electric Vehicle Scheme: Drive green with our EV program
Lucrative Bonus Plan: Enjoy a rewarding bonus structure subject to company or individual performance
Equity Program: Benefit from our equity program with additional options tied to tenure and performance
Career Growth: Explore new opportunities with our internal mobility program
Additional Perks:
According to LinkedIn's Gender Insights Report, women apply for 20% fewer jobs than men, despite similar job search behaviors. At Enable, we’re committed to closing this gap by encouraging women and underrepresented groups to apply, even if they don’t meet all qualifications.
Enable is an equal opportunity employer, fostering an inclusive, accessible workplace that values diversity. We provide fair, discrimination-free employment, ensuring a harassment-free environment with equitable treatment.
We welcome applications from all backgrounds. If you need reasonable adjustments during recruitment or in the role, please let us know.
