Machine Learning Engineer

About Us

At Sapia.ai, we're pioneering the future of ethical, AI-powered hiring. With millions of candidates engaging with our Chat Interview every year, we are redefining how talent is discovered, assessed, and nurtured. Our products are trusted by some of the world's most innovative companies, and we're just getting started.

Why this role exists

We’re not here to build ordinary products. At Sapia.ai, every interaction is designed to be intelligent, fair, and deeply human. Our foundational NLP models set the standard for ethical AI in hiring, but the landscape is evolving at lightning speed. To lead the next wave of innovation, we are infusing our platform with the power of generative AI.

That’s where you come in. As a Machine Learning Engineer, you are the architect of our AI's future. Working closely with Sapia Labs, you’ll be the bridge between cutting-edge research and real-world impact, responsible for building, deploying, and scaling the machine learning systems that power our intelligent chat. This role is for a hands-on builder who thrives in the full ML lifecycle—from data to deployment—and gets satisfaction from shipping robust, scalable AI that matters.

What You'll Do

• End-to-End ML Systems: Design, build, test, and deploy production-grade machine learning models. You will own the entire lifecycle, from data pipelines and model training to deploying scalable inference services on AWS.
• Generative AI Integration: Be at the forefront of our push into generative AI. You will be hands-on with technologies like LangChain and LangGraph to architect and implement the next generation of our conversational AI features.
• Cloud-Native MLOps: Build and maintain our MLOps infrastructure on AWS. You will live in services like ECS for container orchestration and SageMaker for model training and deployment, ensuring our systems are efficient, reliable, and automated.
• Core NLP Enhancement: Uphold and improve the foundational NLP and machine learning models that are the bedrock of our product's fairness and accuracy.
• Technical Collaboration: Work hand-in-hand with data science, product, and engineering to translate vision into technically sound solutions. You’ll be the expert on what it takes to get a model from a notebook into a scalable, production environment.

What You Bring

• 3-5 years in a Machine Learning Engineer or similar role, with a track record of deploying and managing models in a production environment.
• Strong proficiency in Python and a solid understanding of software engineering best practices (e.g., code reviews, testing, CI/CD).
• Technically fluent in the ML/NLP ecosystem (e.g., scikit-learn, spaCy, Hugging Face Transformers).
• Hands-on experience with the AWS platform, specifically with services like ECS and SageMaker. You’re comfortable in a cloud-native world.
• Demonstrable interest and, ideally, practical experience with Large Language Models (LLMs) and frameworks like LangChain or LangGraph.
• Execution-obsessed, with a pragmatic approach to problem-solving and a bias for action to deliver results in a fast paced environment.

How we hire

We believe there’s more to you than your CV. So, we start with a chat interview, our very own, designed to uncover your potential and give you the space to share who you are in your own words. You’ll answer 5-6 role-relevant questions over chat and submit a short video answer. It’s untimed and mobile-friendly. Afterwards, you’ll get personalised insights from the interview, based on your responses. Our hiring team will reach out to you directly if they’d like to move forward.

We’re building something big. Something that matters. If you’re the kind of person who makes things happen, we’d love to hear from you.