Staff Architect, Data & AI Infra

Description About Immunai: Immunai is an AI-driven platform company focused on improving drug discovery and development by decoding the human immune system. We combine large-scale single cell immune data, advanced machine learning, and strong engineering to help pharmaceutical and research partners make better, more informed decisions throughout the drug development process. Our long-term goal is to reduce drug development failure rates and help more effective medicines reach patients. We’re building this platform thoughtfully and collaboratively, bringing together expertise across biology, AI, engineering, and business. Immunai is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. About the role: We are looking for a Staff Architect, Data & AI Infra to shape, build, and scale the infrastructure that powers Immunai's data, AI, and research platforms. This is a senior player-coach role with broad architectural ownership across data infrastructure, ML infrastructure, developer experience, reproducibility, and production reliability. You will work across the wider engineering group as a hands-on technical architect, while also managing a small team of individual contributors focused on ML infrastructure. This role is ideal for someone who can move between long-term platform architecture and practical execution: defining standards, building core systems, mentoring engineers, improving reliability, and partnering with Data Engineering, AI/Research, Product Engineering, Security, Bioinformatics, and Leadership to make Immunai's data and AI platforms scalable, reproducible, secure, compliant, and easier to use. Location: Ramat Gan, Israel (hybrid model) What will you do? Architectural Leadership: Own and evolve the technical roadmap for Immunai’s data and AI platforms, ensuring scalable and reliable architecture that supports current needs and prepares for a multi-cloud future. MLOps & Platform Development: Design and build end-to-end MLOps systems—covering experimentation, training, reproducibility, and deployment—while managing specialized infrastructure like BigQuery, orchestration tools (Dagster/Airflow), and R/Python workloads. Infrastructure Strategy: Define and lead strategy for GPU resources (scheduling, utilization, batch compute) and establish engineering best practices, data architecture standards, and platform guardrails. Developer Experience: Enhance developer productivity by building self-service platforms, automation, internal tooling, and reusable templates that simplify workflows and reduce operational friction. Team Leadership: Act as a player-coach to mentor engineers and manage a small team of ICs, fostering a culture of sound decision-making and technical excellence across the broader group. Security & Reliability: Partner with Security to enforce compliance (SOC2, HIPAA, GDPR) and access controls, while mitigating operational risk through improved observability, incident readiness, and robust support processes. Requirements Required qualifications: 8+ years of industry experience in infrastructure, platform, data, or ML engineering, with a deep background in designing production infrastructure for data-intensive or AI/ML systems. Hands-on expertise building and operating MLOps systems (for model development, training, and deployment) and managing GPU infrastructure, including scheduling, resource management, and utilization. Proficient in managing data infrastructure technologies (e.g., BigQuery, data warehouses, object storage, orchestration systems like Dagster or Airflow) and operating within Kubernetes/containerized environments. Demonstrated ability as a player-coach, including people-management experience or leading small engineering teams, with a focus on mentoring senior engineers and influencing technical direction. Strong communication skills with the ability to partner effectively across diverse groups, including Data Engineering, AI/Research, Product Engineering, Security, Bioinformatics and Leadership. Preferred qualifications: Developer Platform & Velocity: Proven ability to build internal developer platforms, "golden paths," and self-service infrastructure that reduce operational friction and streamline workflows for research and engineering teams. AI-First Transformation: Experience leading or guiding software and data engineering teams through the transition toward AI-first development processes, fostering adoption of new paradigms and tooling. Compliance & Domain Expertise: Strong background operating within regulated environments (SOC2, HIPAA, GDPR) and applying infrastructure best practices to domain-specific fields such as biotech, life sciences, or bioinformatics. Desired personal traits: You want to make an impact on humankind You prioritize “We” over “I” You enjoy getting things done and striving for excellence You collaborate effectively with people of diverse backgrounds and cultures You have a growth mindset You are candid, authentic, and transparent When you apply for a role at Immunai, we process your personal data (such as CV, contact details, and professional background) to assess your suitability for current and future roles. 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