Machine Learning Engineer, Ranking & Retrieval

📋 Job Overview

There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting. Our first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.

🏢 About ActAI

ActAI is building proactive AI-native applications for everyday use, focusing on integrating intelligence into daily workflows with minimal prompting.

🎯 The Role

Build and improve ML systems that power our AI-native applications. Work on classification, retrieval, ranking, personalization and evaluation, taking ownership from model development to production deployment. This role is for engineers who enjoy solving practical ML problems, improving model quality and shipping reliable systems used by real users.

✅ Key Responsibilities

• Build and improve ML models for classification, ranking, retrieval and personalization.
• Develop efficient classifiers and optimize model accuracy, precision, latency and cost.
• Improve search relevance and context retrieval using embeddings, hybrid search and reranking.
• Build personalization models that learn from user behavior while respecting privacy.
• Develop evaluation pipelines, labeled datasets and metrics to measure model quality.
• Fine-tune and adapt existing models when supported by sufficient data.
• Build and maintain ML training, inference and deployment pipelines.
• Debug production issues, monitor model performance and ship improvements based on real-world feedback.

📌 Required Qualifications

• Strong foundations in machine learning and modern neural architectures.
• Hands-on experience building and deploying ML models in production.
• Experience in classification, ranking, retrieval, recommendation systems or personalization.
• Understanding of model evaluation, experimentation and performance metrics.
• Comfortable writing production-quality code and working with real-world datasets.
• Able to balance model quality, latency, cost and reliability.
• Strong ownership, problem-solving skills

⭐ Desirable Experience

• Experience with Python and frameworks like PyTorch or JAX.
• Experience with embeddings, vector search, and ranking models.
• Experience with production ML systems and inference pipelines.

🎁 Benefits

Competitive salary and benefits package.

🛂 Visa & Eligibility

Visa sponsorship may be available.