Principal Machine Learning Engineer

Algolia is a fast-growing company that helps users deliver an intuitive search-as-you-type experience on their websites and mobile apps. We provide a search API used by thousands of customers in more than 100 countries. Today, Algolia powers 1.5 Trillion searches a year – that’s 4 times more than Bing, Yahoo, DuckDuckGo, Baidu and Yandex combined!

As a Principal Machine Learning Engineer in the AI Search & Discovery Group, you’ll play a critical role in scaling the ML foundations that power Algolia’s relevance, ranking, recommendations, and generative AI experiences. This is a hands-on, high-impact role for an engineer who thrives on solving complex technical problems while enabling others to move faster and go further.

Join the AI Search & Discovery Group and help shape the next generation of intelligent retrieval, matching, and discovery experiences.

Our AI systems span multiple domains—from lexical search to semantic retrieval, from dynamic re-ranking to personalized recommendations, and from offline pipelines to real-time inference. As these systems grow, so does the opportunity to build high-leverage components that multiply the impact of everyone working on AI at Algolia.

We’re looking for an experienced engineer with both deep technical expertise and a strong sense of care—someone who loves building things, thrives in ambiguity, and finds joy in helping others succeed. Whether you’re contributing to shared infrastructure, shaping architecture, or mentoring engineers, your work will help unlock faster iteration, greater reliability, and more cohesive AI systems across teams.

Your role will consist of:

• Designing and implementing scalable, reusable, and high-performance ML systems that span multiple areas of AI Search & Discovery
• Collaborating across teams to accelerate experimentation, streamline deployment, and improve the quality and reliability of models in production
• Driving the evolution of shared infrastructure, tools, and components—such as embedding services, inference pipelines, and evaluation frameworks
• Guiding architectural decisions that balance long-term flexibility with near-term impact
• Acting as a connector across domains and teams, identifying opportunities for reuse and alignment
• Mentoring engineers with empathy—bringing clarity, unblocking complexity, and helping others grow in their own areas of excellence

You might be a fit if you:

• Have a strong engineering background with extensive experience in applied machine learning or deploying ML systems in production.
• Have led the design and deployment of models or pipelines in one or more of: search, recommendation, personalization, NLP, or GenAI—and can adapt across domains as needed
• Love balancing hands-on technical problem solving with empowering others to do their best work
• Communicate clearly, work collaboratively, and know how to navigate ambiguity in fast-paced environments
• Write clean, production-grade code in Python (and ideally Go), and care about correctness, scalability, and developer experience
• Are deeply curious and motivated by impact—both through systems you build and people you support

Ideally you would also have:

• Experience with real-time ranking systems, retrieval-augmented generation, or reinforcement learning for recommendations
• Familiarity with modern ML Ops stacks, including feature stores, offline/online evaluation tooling, and distributed inference
• Experience in cross-team initiatives or open-source contributions that required both technical depth and organizational alignment

We’re looking for someone who can live our values:

• GRIT - Problem-solving and perseverance capability in an ever-changing and growing environment
• TRUST - Willingness to trust our co-workers and to take ownership 
• CANDOR - Ability to receive and give constructive feedback.
• CARE - Genuine care about other team members, our clients, and the decisions we make in the company.
• HUMILITY- Aptitude for learning from others, putting ego aside.