Software Engineer (Data Lifecycle)

Location: On-site in New York City, United States

Base salary: $200K–$400K plus equity

Company

Decagon builds a conversational AI platform for enterprise customer experiences across voice, chat, email, SMS, and other channels.

What you will do

• Build PII handling and redaction systems for conversations, agent traces, images, attachments, voice notes, documents, and other customer data.
• Design abstractions for configurable redaction and retention policies with safe defaults for enterprise requirements.
• Build reliable deletion workflows across storage systems and derived data, including retries, verification, failure visibility, backfills, and safe rollouts.
• Develop self-serve tools and APIs for configuring, testing, and understanding data lifecycle policies.
• Build LLM-backed anomaly and policy-violation detection systems and train or tune models for PII and PHI detection.
• Implement monitoring and auditability that show what data was processed, what remains, and where policies failed without exposing sensitive information.

Requirements

• 4+ years of experience building software products, with strong product thinking.
• Proficiency in Python and comfort working across the entire stack.
• Experience designing reliable backend systems and data pipelines, including asynchronous processing, retries, and failure recovery.
• Understanding of operational maturity for critical systems, including observability and monitoring.
• Ability to reason about data flows, persistent copies, and failures across complex workflows.
• Ability to debug system failures in deep technology stacks using appropriate tools.
• Judgment about when to ship quickly and when correctness, privacy, and irreversible operations require additional caution.

Nice to have

• Experience with PII detection, redaction, tokenization, or secure handling of sensitive data.
• Experience building data retention, deletion, or privacy-request workflows across multiple storage systems.
• Experience with authentication, authorization, data access controls, or other security-sensitive product systems.
• Familiarity with GDPR, CCPA, PCI, and translating privacy requirements into engineering controls.
• Background with LLMs, evaluations, or AI agent systems handling sensitive data in model inputs, outputs, and traces.
• Experience with observability, audit trails, or operational tooling for large-scale data systems.
• Experience working with sensitive voice, image, or other non-text data.

Culture & Benefits

• In-office work with an emphasis on execution, invention, excellence, and velocity.
• Medical, dental, vision, life insurance, and disability benefits for full-time employees.
• Retirement plan, parental leave, and fertility and family-building benefits.
• Monthly wellness and lifestyle stipend, daily office lunches and snacks, and a take-what-you-need vacation policy subject to local requirements.
• Competitive equity in addition to base salary; compensation may vary by location within the United States.