Focus on building applications to enable more real world impact and highest usage for the world. This is a remote role based in Indonesia, working closely with our HQ in Malaysia and cross-functional regional teams. You’ll operate across the stack, from backend logic and integration to frontend delivery, building intelligent systems that scale fast and matter deeply.
Drive business growth by building and optimizing the recommendation systems that personalize experiences for millions of users daily. You’ll own the modeling, feature engineering, data pipelines, and experimentation that make personalization smarter, faster, and more impactful. This role gives you the chance to work on systems serving 100M+ predictions daily, directly impacting engagement, retention, and revenue at scale.
In this role, you’ll work at the intersection of advertising algorithms, personalization, and optimization, building the ML software and infrastructure that drives ad performance and user engagement at scale. Elevate Launch Potato’s advertising performance by building scalable, production-ready ML systems that optimize revenue while enhancing user experience.
Play a pivotal role in building, scaling, and leading the next generation of intelligent systems. This role blends deep technical expertise with hands-on leadership, reporting directly to the Head of AI. You’ll write production code, shape strategy, mentor team members, and help grow a lean, world-class AI engineering team.
Revolutionizing cybersecurity by combining human intelligence with artificial intelligence to create the world's most effective offensive security platform. As a Staff Software Engineer on our AI Platform team, you'll be at the forefront of developing cutting-edge AI-powered security solutions that protect organizations from emerging threats. You'll work directly on our AI platform, contribute to our in-platform AI security agent “Hai,” and help build next-generation AI safety and security tools that serve thousands of companies.
You will own the architecture, development, and continuous improvement of Sully.ai’s NLP models powering the Receptionist and Assistant agents. Working cross‑functionally with Product, Clinical, and Reliability Engineering, you’ll translate clinical workflows into robust conversational AI solutions that meet HIPAA‑level security and compliance requirements.
In this role as Sr. Staff Machine Learning Engineer, you will define and lead the architecture and development of large-scale, foundational machine learning systems that power care personalization, treatment intelligence, and automation at scale. Your work will directly shape MedMatch—our AI-powered system—by owning the architecture and direction of key ML components, enhancing provider-patient interactions, optimizing treatment recommendations, and expanding into new verticals.
In this role as Principal Machine Learning Engineer, you will define and drive the architectural vision for large-scale, foundational machine learning models and applications that power care personalization, treatment intelligence, and automation at scale. Your work will directly impact every aspect of patient care, including but not limited to MedMatch, our AI-powered system that enhances provider-patient interactions, optimizes treatment recommendations, and expands into new verticals.
As an AI/ML Engineer, you will lead the development and deployment of computer vision and predictive models, ensuring they’re not only high-performing but also secure, scalable, and compliant with healthcare regulations like HIPAA and SOC 2. You’ll work cross-functionally to bring AI from concept to production—optimizing models, integrating them into clinical workflows, and driving innovation at the edge of technology and patient care.
Work with product managers and stakeholders to define AI and machine learning objectives, requirements, and timelines. Design, develop, and implement AI models, algorithms, and applications to solve complex business challenges. Oversee the full AI model lifecycle, including data collection, preprocessing, model training, evaluation, and deployment.