Senior Software Engineer, Quality Engineering

Alpaca

Get hot jobs first on Telegram

New positions appear faster in our channel

Location: Global
Job Type: full-time
Work Format: 🌍 Remote Worldwide
Salary: Not specified
Posted: October 14, 2026

Job Description

Who We Are:

Alpaca is a US-headquartered, global leader in agent-first brokerage infrastructure for stocks, ETFs, options, crypto, fixed income, 24/5 trading, and more.

Amongst our subsidiaries, Alpaca is a licensed financial services company, serving hundreds of financial institutions across 40 countries with our institutional-grade APIs. This includes broker-dealers, investment advisors, wealth managers, hedge funds, and crypto exchanges, totalling over 10 million brokerage accounts.

Our global team is a diverse group of experienced engineers, traders, and brokerage professionals who are working to achieve our mission of opening financial services to everyone on the planet. We're deeply committed to open-source contributions and fostering a vibrant community, continuously enhancing our award-winning, developer-friendly API and the robust infrastructure behind it.

Alpaca is proudly backed by $400 million in funding from top-tier global investors including Portage Ventures, Spark Capital, Tribe Capital, Social Leverage, Horizons Ventures, Opera Tech Ventures, SBI Group, Derayah Financial, Unbound, Peak XV, Elefund, and Y Combinator.

Our Team Members:

We're a dynamic team of 400+ globally distributed members who thrive working from our favorite places around the world, with teammates spanning the USA, Canada, Japan, Hungary, Nigeria, Brazil, the UK, and beyond!

We're searching for passionate individuals eager to contribute to Alpaca's rapid growth. If you align with our core values—Stay Curious, Have Empathy, and Be Accountable—and are ready to make a significant impact, we encourage you to apply.

Your Role

As a Senior Software Engineer on the Platform team, you will build and own the frameworks, tooling, and CI/CD infrastructure that let Alpaca’s engineers ship new features quickly and reliably. This is a software engineering role at its core: you will spend most of your time writing Go, designing automated test frameworks, and digging into application code and Kubernetes when needed. Just as importantly, you will help redefine how we use AI to raise the bar on quality - leveraging LLMs and AI-driven tooling to generate tests, catch regressions before they ship, and turn every incident into durable, automated coverage. We are looking for engineers who treat quality as an engineering discipline and see AI as a force multiplier for it - people who build the systems that make testing fast, reliable, and effortless for the teams that depend on them.

Things You Get To Do

Design and build automated test frameworks across our REST and gRPC API surface.
Leverage AI and LLM-based tooling to accelerate test generation, harden regression coverage, and turn production incidents into automated tests - embedding these practices into the frameworks the whole engineering org relies on.
Build and maintain CI/CD pipelines - we use a mix of GitHub Actions and Google Cloud Build.
Design and execute load, stress, and scalability tests to ensure our platform can handle growing demand.
Introduce chaos engineering practices - designing fault-injection and resilience experiments that surface failure modes before our customers do.
Standardize tooling and infrastructure to improve the day-to-day developer experience.
Partner with other teams to raise test coverage, reliability, and speed across services.
Dig into application code to root-cause failures and drive fixes to completion.
Mentor and guide product engineers, fostering a culture of quality and continuous improvement.

Who You Are (Must-Haves)

You have 5+ years of experience as a software engineer building and maintaining production systems.
You have written code in strongly typed languages and have at least 1 year of experience with Go.
You are fluent with AI-assisted engineering - you actively use LLM-based tools in your workflow and have a clear point of view on where they elevate quality (and where they don’t).
You have built and maintained CI pipelines before - ideally with GitHub Actions or Google Cloud Build.
You have designed and built automated test frameworks, and you understand the challenges microservices pose for reliable integration and end-to-end testing.
You are familiar with Kubernetes beyond the basics - you can write a Helm chart for a new service with relative ease, you understand best practices around security, and you have prior job experience or certifications to prove this.
You have worked at a startup before - you understand that things don’t always go according to plan, that there is significant technical debt, that some tasks just need to be done, and that sometimes there are no processes or documentation to guide your hand.
You are not afraid of taking a task from start to finish - gathering requirements, figuring out and implementing the best technical solution, then deploying and monitoring the results.

Who You Might Be (Bonus Points)

You have used AI/LLM tooling to build QA automation - test generation, flaky-test triage, or incident-to-regression pipelines - and can speak to what worked and what didn’t.
You have written code in Python or TypeScript, and can build efficient Dockerfiles and secure container images.
You have implemented load tests with k6 (REST) and ghz (gRPC) and have experience analyzing pprof/trace output.
You have done IaC deployments with Argo CD or Terraform, or have used Google Cloud Platform and Google Kubernetes Engine before. You are familiar with Tilt and have written a Kubernetes Operator before.
You have practiced chaos engineering - running fault-injection or resilience experiments with tools like Chaos Mesh, LitmusChaos, or Gremlin.
You are familiar with testing event-driven systems using Kafka (or even better, Redpanda).

How We Take Care of You:

Competitive Salary & Stock Options
Health Benefits
New Hire Home-Office Setup: One-time USD $500
Monthly Stipend: USD $150 per month via a Brex Card

Alpaca is proud to be an equal opportunity workplace dedicated to pursuing and hiring a diverse workforce.

Recruitment Privacy Policy

🎯 Who is this job for?

Level: Senior Software Engineer specializing in Quality Engineering and platform infrastructure.

The role suits someone with 5+ years of production software engineering experience, including strong skills in Go, AI-assisted engineering, automated test framework design, CI/CD, Kubernetes, Helm, microservices, and startup environments.

They should be familiar with building REST and gRPC test automation, load and stress testing, chaos engineering, Docker, cloud and infrastructure tooling, debugging application failures, improving developer workflows, and mentoring engineers.

💬 Potential Interview Questions

1. How would you design an automated test framework for Alpaca’s REST and gRPC APIs?

I would build a reusable Go-based framework with shared clients, authentication, fixtures, assertions, cleanup, and configurable environments. It should support contract, integration, negative, and end-to-end tests while producing clear reports and diagnostics for CI.

2. What strategies would you use to make microservices integration tests reliable and non-flaky?

I would isolate dependencies where appropriate, use deterministic test data, explicit readiness checks, controlled retries, and proper cleanup. For critical workflows, I would combine service virtualization with a smaller number of production-like end-to-end tests.

3. How would you structure a GitHub Actions or Google Cloud Build pipeline for a Go service?

The pipeline should run formatting, static analysis, unit tests, integration tests, security checks, and container builds in parallel where possible. I would use dependency caching, immutable artifacts, environment-specific deployment gates, and clear failure diagnostics to keep feedback fast and actionable.

4. How would you use LLMs to improve software quality without blindly trusting generated tests?

LLMs can generate test cases from API contracts, code changes, incident reports, and historical failures, but every generated test needs deterministic execution and human or automated validation. I would measure mutation coverage, defect detection, flakiness, and maintenance cost to determine whether the AI-generated tests provide real value.

5. How would you design load and stress tests for REST and gRPC services using k6 and ghz?

I would model realistic traffic mixes, authentication flows, payload sizes, concurrency, and error conditions, then establish baseline latency, throughput, and resource metrics. I would use k6 for HTTP workloads, ghz for gRPC workloads, and correlate results with CPU, memory, database, queue, and Kubernetes metrics.

6. What would you investigate when a service shows increased latency during a load test?

I would compare application metrics with infrastructure and dependency metrics, then inspect Go pprof profiles and execution traces for CPU, memory, blocking, and goroutine issues. I would also check connection pools, database queries, network calls, Kubernetes throttling, autoscaling behavior, and tail latency rather than relying only on averages.

7. What are important considerations when creating a secure Helm chart for a service?

The chart should use non-root containers, read-only filesystems where possible, minimal Linux capabilities, resource requests and limits, health probes, and restricted network access. Secrets should be referenced securely rather than embedded in templates, and configuration should support predictable rollbacks and environment-specific overrides.

8. How would you introduce chaos engineering into a Kubernetes-based platform?

I would begin with explicit reliability hypotheses and controlled experiments, such as pod termination, network latency, dependency failures, or resource pressure. Experiments should run in safe environments with observability, abort conditions, measurable success criteria, and follow-up regression tests for every discovered failure mode.

9. How would you test an event-driven workflow that uses Kafka or Redpanda?

I would verify schemas, partitioning, ordering guarantees, retries, idempotency, consumer-group behavior, offsets, dead-letter handling, and duplicate delivery. Tests should control topics and offsets, use deterministic events, and validate both eventual outcomes and operational metrics such as lag.

10. How would you turn a production incident into durable automated coverage?

First, I would reproduce the failure using the incident timeline, logs, traces, inputs, and dependency state, then encode the smallest deterministic regression test that captures the root cause. I would add it to the appropriate framework and CI stage, improve observability if needed, and verify that the test fails against the vulnerable version and passes after the fix.

📋 Job Summary

Alpaca is a global, US-headquartered brokerage infrastructure company serving millions of financial accounts worldwide. As a Senior Software Engineer, Quality Engineering, you’ll build Go-based test frameworks, CI/CD pipelines, load and chaos testing, Kubernetes tooling, and AI-powered quality automation across REST and gRPC services. This is a fully remote, global role using Go, GitHub Actions, Google Cloud Build, Kubernetes, Helm, Docker, GCP, and LLM tooling. Alpaca offers a competitive salary with stock options, health benefits, a $500 home-office setup budget, and a $150 monthly stipend—plus the chance to shape quality engineering at a fast-growing fintech.