About the team

The Dev Accelerator team builds and operates our internal developer platform: the tools, libraries, and infrastructure that let 100+ engineers design, build, test, and ship products quickly and safely.

We own:

• Developer CLI tooling – a “Prompt-to-Product” style CLI that scaffolds services end-to-end, environment/credentials tooling, and a wide range of internal developer scripts.

• Monorepo build, lint, and CI infrastructure, including our core backend CI workflow, Bazel integration, and a custom linting framework and rule set used across the codebase.

• Go ecosystem stewardship for a large suite of shared libraries and applications under our main Go module.

• Python shared libraries & frameworks for core business logic, gRPC helpers, utilities, and standardized component libraries (datastores, messaging, web, etc.).

• Engineering effectiveness & test infrastructure, including automated canary analysis, test data and coverage tooling, bad test dashboards, dependency analysis, and “broken main” automation.

Our goal is to make the easy path the right path for product teams and AI agents building on the platform.

About the role

As a Software Engineer 2, Dev Accelerator

, you will:- Design and evolve theinternal developer platform that underpins virtually all backend development.

• Own high-leverage projects across CLI tooling, CI/CD, shared libraries, and test infrastructure.

• Work across Python, Go, Bazel, Kubernetes, AWS, and CI systems to make our Golden Path fast, reliable, and intuitive.

• Shape abstractions and workflows that are AI-native, consumable by both humans and AI agents.

You’ll operate as an IC, leading multi-quarter initiatives and collaborating with product teams, infrastructure, SRE, and Security.

What you’ll do

• Build and evolve developer CLI tooling

• Extend our primary developer CLI to scaffold new component types and services end-to-end (service manifests, container/build configs, deployment charts, build files, API definitions, starter code, alerts, runbooks).

• Improve environment and credentials tooling to make local development setup fast and reliable.

• Own core CI/CD and linting infrastructure

• Design and maintain the backend CI workflow used by backend services.

• Evolve linting, formatting, and typing tools to enforce architectural and code-quality guardrails across the monorepo.

• Debug and fix CI issues that block engineers, and proactively reduce flakiness and runtime.

• Steward shared Go and Python ecosystems

• Own key shared Go libraries (auth, caching, clients, configuration, cryptography, logging, metrics, domain/realm, server, etc.) and their usage across many applications.

• Maintain and evolve Python shared libraries and frameworks in our core libraries, gRPC helpers, utilities, and standardized components.

• Strengthen test and release safety

• Extend automated canary analysis with new metric types, backtesting, and safer defaults.

• Build and improve test automation tooling, bad-test detection dashboards, and dependency-analysis utilities to keep main green and tests reliable.

• Contribute to automation that classifies CI failures and summarizes them for engineers (including LLM-assisted workflows).

• Drive platform-level design and abstractions

• Design abstractions that balance simplicity for product engineers with enough power for advanced use cases.

• Collaborate with PM/TPM, infrastructure, and product teams to scope and deliver multi-team initiatives (e.g., prompt-to-product workflows, typing and linting initiatives, test automation).

Required technical skills

Languages & frameworks

• Strong experience with Python 3.x:

• CLI development (e.g., Click or similar).

• YAML/Jinja2-style templating.

• Modern type hints and typing discipline (e.g., typing, dataclasses / attrs, Pydantic-style patterns).

• Testing with pytest or similar.

• Solid experience with Go:

• Shared library and service development (gRPC/HTTP).

• CLI patterns (e.g., Cobra/Viper or equivalents).

• Testing with Go testing frameworks (e.g., Ginkgo/Gomega or the standard library).

Protocols & APIs

• Protobuf/gRPC:

• Schema design and evolution.

• Cross-language client/server generation and integration.

Build systems & infrastructure

• Bazel in a large monorepo:

• BUILD rules and dependency management.

• Working with code generation for APIs and clients.

• Containers & orchestration:

• Docker image builds.

• Kubernetes concepts (Helm-style values, service deployments, readiness/liveness/health checks).

• Cloud infrastructure (AWS preferred):

• Experience with a meaningful subset of: object storage, relational databases (e.g., Postgres), key–value/document stores, search, streaming/ingest services, Kafka, Redis, IAM.

CI/CD & DevOps

• Modern CI systems (e.g., Git-based CI/CD platforms):

• Authoring non-trivial pipelines (matrix builds, reusable workflows, secrets/permissions).

• Infrastructure-as-Code mindset:

• Comfortable working with service manifests (YAML), Terraform/Terragrunt-like patterns, or internal equivalents.

• Linting & static analysis:

• Hands-on experience configuring and tuning linting and typing tools (e.g., pylint, ruff, mypy, golangci-lint).

• Experience writing or extending custom lint rules is a strong plus.

Strongly preferred

• Monorepo experience

• Worked in a large, shared codebase with complex dependency graphs and shared frameworks.

• Familiar with dependency graph analysis and strategies to keep builds/tests fast.

• Developer experience (DX) tooling

• Built or maintained CLI tools, scaffolding systems, or internal frameworks used by other engineers.

• Thoughtful about ergonomics, documentation, and guardrails.

• Testing & progressive delivery

• Exposure to canary analysis / progressive rollout systems (e.g., Prometheus/PromQL, Grafana, automated deployment checks).

• Experience with test data management, integration/E2E test infrastructure, or bad-test detection.

• Messaging & streaming

• Experience with Kafka (topic design, producers/consumers, observability, error handling).

• LLM/AI integration

• Experience or strong interest in using LLMs to improve developer workflows (e.g., failure summarization, smart code generation, AI-native CLIs).

How you work

We’re looking for someone who:

• Designs scalable developer abstractions

• Has built tools that many engineers depend on, and can point to where those abstractions worked (or didn’t) and why.

• Operates across the full platform stack

• Comfortable jumping between Python CLIs, Go libraries, Bazel rules, YAML manifests, CI workflows, and Kubernetes deployments.

• Reduces complexity for others

• Measures success by how much simpler and safer life becomes for product engineers and AI agents using the platform.

• Thinks in terms of leverage

• Prefers building guardrails, automation, and self-service over manual ops work.

• Writes clear documentation

• Treats usage and developer documentation as part of the product surface, not an afterthought.

• Collaborates across teams

• Communicates clearly with product teams, infra, SRE, and security; comfortable gathering feedback via Slack, ticketing systems, and design docs and iterating quickly.

If you’re excited about building the tools and infrastructure that hundreds of engineers (and AI agents) will use every day, we’d love to talk.

At Abnormal AI, certain roles are eligible for a bonus, restricted stock units (RSUs), and benefits. Individual compensation packages are based on factors unique to each candidate, including their skills, experience, qualifications and other job-related reasons.

Base salary range:$148,800—$175,000 USD

Abnormal AI is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status or other characteristics protected by law. For our EEO policy statement please click here. If you would like more information on your EEO rights under the law, please click here.