Acquire - SW Engineer (AI)

Location: Remote, Tel Aviv-Yafo, Israel

Company

hirify.global develops AI-driven customer growth and value-optimization solutions that help companies acquire, nurture, and retain high-value customers through platforms such as Google, Meta, Salesforce, and Braze.

What you will do

• Transform raw customer data into ad-network conversions across ML and data pipelines, event-driven delivery services, and customer-facing UI.
• Take features from idea to production independently by designing, shipping, monitoring, and iterating on systems.
• Use AI and agentic tooling as part of the engineering workflow.
• Own production systems in a correctness-critical domain where defects can directly affect customer advertising spend.
• Collaborate with the team lead, product, and data scientists to move models and ideas from prototypes to reliable, scalable production.

Requirements

• 3+ years of hands-on experience building production cloud systems end to end, including architecture, development, testing, and cloud-native production work.
• Strong Python and solid software engineering foundations, including software design principles, concurrency, data structures, and cost/performance trade-offs.
• Use of, or strong willingness to adopt, AI and agentic tooling as a core part of shipping.
• Comfort working across the full stack and taking end-to-end ownership on a small team.
• Teamwork, excellent communication, independent learning ability, curiosity, and continuous improvement.

Nice to have

• SQL and modern data warehouses such as Snowflake, BigQuery, or Databricks equivalents.
• Experience with ad-network, martech, attribution, or measurement ecosystems, including Google Ads, Meta, MMPs, or conversion APIs.
• Experience with Temporal, Airflow, Docker, Kubernetes, ArgoCD, Terraform, and AWS.
• Background in analytics, data science, or product, with the ability to assess whether signals are correct and valuable.

Culture & Benefits

• Remote work arrangement within the Tel Aviv, Israel role location.
• AI-native R&D environment using AI and agentic tooling as a first-class part of development.
• High-ownership work in a small team with broad responsibility across the technology stack.
• Collaboration with product and data science specialists.