Sr. Software Engineer in Test

At Replicant, we believe AI should work for people, starting with customer service. We’re looking for a Software Development Engineer in Test (SDET) to help raise the bar for quality across our voice AI platform as we continue to scale. This person will play a key role in shaping how quality is defined, measured, and built into the product experience from the start — across platform capabilities, AI agents, customer configurations, integrations, and release workflows. This is an opportunity to help build the systems, standards, and tooling that make quality a durable advantage for both our customers and our teams.

As a SDET you will partner closely with Engineering, Product, and Delivery to create a more unified and scalable approach to quality. You’ll define standards, improve testing strategy, build and evolve automation and platform-level tooling, and turn customer and product insights into better validation systems. This is a hands-on engineering role for someone who is excited to work at the intersection of software quality, automation, analytics, and AI. The right person will help us move toward a modern quality engineering practice where testing is proactive, automated wherever possible, and grounded in the realities of conversational AI.

What You'll Do

- Act as a senior technical authority for quality engineering at Replicant; shaping how quality is built into the platform through hands-on engineering, championing a high bar across teams, and driving the practices and execution that make it real.

- Raise the technical bar across our team of AI-enabled QA Engineers through hands-on example, code and test review, pairing, and mentorship; leveling up how a small team builds a scalable quality engineering practice.

- Help drive Replicant's AI-native quality engineering strategy to scale coverage, accelerate validation, and surface failures earlier.

- Build AI-driven testing workflows that let a small team deliver broad, scalable validation across the platform.

- Own the quality feedback loop from internal dogfooding, customer A/B testing, customer feedback triage, and bug reporting, turning real-world usage into faster fixes, better test coverage, and stronger product decisions.

- Partner with Product and Engineering early to improve testability, shape acceptance criteria, and build quality in from the start.

- Build and improve automation frameworks, validation tooling, and CI/CD quality gates for reliable, repeatable releases.

- Use AI and analytics to expand edge-case coverage, summarize failures, and generate insights that inform roadmap and release decisions.

What You'll Bring

- 5+ years in SDET or quality engineering roles, with significant hands-on coding, including ownership of test strategy and release quality in complex software systems.

- Strong programming skills in at least one modern language (TypeScript, JavaScript, or Python), with the ability to build and own production-quality tooling and frameworks.

- An AI-native approach to quality engineering. You know how to leverage AI to move faster, increase coverage, reduce manual effort, and build smarter testing and validation workflows.

- Curiosity about AI systems, conversational experiences, and the nuances of testing voice-driven product behavior.

- Experience building quality initiatives across teams and influencing engineers, product managers, and stakeholders around a shared quality bar.

- Deep knowledge of modern testing practices, including automation, API and integration testing, regression strategy, and CI/CD-based quality workflows.

- Demonstrated mastery of test design, risk-based coverage, and what should be automated versus explored manually.

- Comfort using data, metrics, and analytics to evaluate quality and communicate trends, risk, and opportunities for improvement.

- Excellent collaboration and communication skills, with the ability to work effectively across Engineering, Product, and customer-facing teams.

Nice To Have

- Experience testing AI and LLM-powered systems, including evaluating non-deterministic behavior, hallucinations, prompt or workflow regressions, and other quality issues unique to generative products.

- Experience working in an FDE, delivery, or professional services environment, with a strong understanding of customer-facing implementation workflows and real-world quality challenges.