# Staff Software Engineer, AI Gateway
**Company:** [Docker](https://scaleengineer.com/companies/docker)
Staff Software Engineer specializing in AI Gateway infrastructure at Docker, a leading containerization platform trusted by 20M+ monthly users. You'll architect scalable, secure systems for AI agent orchestration, policy enforcement, and observability across diverse LLM providers, requiring 8+ years of backend distributed systems expertise in Go/Rust with proven ownership of production-grade infrastructure at significant scale.
**Role:** Staff Engineer
**Seniority:** Staff
**Locations:** United States
**Remote:** yes
**Salary:** 170350–275550 USD
[Apply](https://jobs.ashbyhq.com/docker/30babe0a-57e8-4425-8991-8797e8337e82)
Canonical: https://scaleengineer.com/jobs/docker/staff-software-engineer-ai-gateway
---
## Responsibilities

- Set Technical Direction for Core AI Gateway Systems: Define and execute architectural strategy for the model router, MCP gateway, and control plane. Make critical architectural decisions that scale to 10x usage growth while maintaining system reliability, performance, and security. Partner closely with engineering leadership to translate product vision into technical strategy that guides the team's execution roadmap.
- Own Mission-Critical System Components: Take full ownership of the highest-impact, highest-complexity problems including dynamic model selection algorithms, auto-failover mechanisms, cost-based routing logic, and end-to-end policy enforcement systems. Establish yourself as the go-to expert for secure credential storage, session summarization, caching strategies, and rate limiting infrastructure that the entire system depends on.
- Identify and Eliminate Infrastructure Gaps: Proactively scan the system landscape to identify architectural bottlenecks, process inefficiencies, and tooling gaps before they become production blockers. Drive cross-team solutions from initial problem identification through design, implementation, and production deployment. Advocate for systemic improvements that benefit the entire organization, not just individual teams.
- Build AI Agent Evaluation and Analysis Infrastructure: Architect the foundational systems that enable comprehensive evaluation, replay, and backtesting of agent and LLM behavior with production-grade confidence. Design infrastructure that supports both internal Docker decision-making and customer observability needs, enabling data-driven optimization of agent applications.
- Mentor and Elevate Technical Excellence: Actively mentor senior and mid-level engineers, raising the technical bar through rigorous code review, architectural guidance, design pattern documentation, and hands-on example. Establish and enforce engineering standards that become the organizational baseline, ensuring the team consistently produces maintainable, secure, and performant systems.
- Shape Product Direction Through Technical Leadership: Collaborate with product, design, and executive leadership to inform not just how the control plane UI functions, but where the entire product roadmap evolves. Translate customer needs and technical constraints into concrete feature specifications and architecture improvements that drive product differentiation.
- Design Unified Abstraction Across AI Model Providers: Architect the foundational abstraction layer that normalizes interactions with diverse LLM providers (OpenAI, Anthropic, Google, AWS Bedrock). Ensure this abstraction is extensible, maintainable, and becomes the standard upon which the entire organization builds, reducing complexity and accelerating feature development.
- Implement Enterprise-Grade Security and Compliance: Build secure, scalable identity and credential vaulting systems that support enterprise customer requirements. Ensure policy enforcement, audit logging, and security controls work seamlessly end-to-end from prompt input through tool execution to final response, eliminating compliance gaps that auditors or attackers could exploit.

## Requirements

### education

- {"name":"Bachelor's Degree in Computer Science or Engineering","description":"Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or related technical field from an accredited institution, or equivalent practical professional experience demonstrating equivalent foundational knowledge."}

### technical

- {"name":"Distributed Systems Architecture","description":"Advanced expertise designing and implementing distributed systems that operate reliably at scale. Deep understanding of consistency models, failure modes, load balancing, fault tolerance, and scalability patterns. Proven ability to architect systems that handle 10x growth while maintaining performance SLAs and operational simplicity."}
- {"name":"Backend Systems in Go or Rust","description":"Expert-level proficiency in Go or Rust for building production backend systems. Demonstrated mastery of concurrency models, memory management, performance optimization, and the ecosystem tools surrounding these languages. Experience shipping high-performance, production-grade backend services."}
- {"name":"Cloud-Native Infrastructure and Kubernetes","description":"Deep hands-on experience with cloud-native architectures, containerization, and Kubernetes orchestration. Ability to design systems that leverage cloud platform capabilities (networking, storage, security, observability) and operate efficiently in distributed container environments."}
- {"name":"API Gateway and Proxy Architecture","description":"Specialized expertise building secure, high-throughput API gateways, reverse proxies, and similar control plane infrastructure. Understanding of request routing, load balancing, policy enforcement, authentication integration, rate limiting, and observability in gateway contexts. Experience with systems like Kong, Envoy, or comparable solutions."}
- {"name":"Secure Credential and Secret Management","description":"Advanced knowledge of credential vaulting, secret rotation, identity management, and secure storage patterns. Understanding of OAuth 2.1 flows, IAM systems, certificate management, and cryptographic best practices. Experience implementing systems that meet enterprise security auditing requirements."}
- {"name":"Observability and Monitoring Systems","description":"Expert-level understanding of observability patterns including distributed tracing, metrics collection, structured logging, and real-time alerting. Proficiency with frameworks like OpenTelemetry. Ability to design systems that provide operational visibility while minimizing performance overhead."}
- {"name":"Policy Engines and Enforcement","description":"Experience with policy-as-code frameworks and policy enforcement systems. Familiarity with solutions like OPA (Open Policy Agent) or comparable platforms. Understanding of how to build policy evaluation into systems without becoming a bottleneck, and how to ensure policy compliance across complex workflows."}
- {"name":"LLM Integration and AI Agent Frameworks","description":"Working knowledge of large language model APIs (OpenAI, Anthropic, Google), LLM integration patterns, and AI agent orchestration frameworks. Understanding of RAG architectures, vector databases, function calling, and prompt engineering concepts from an infrastructure perspective."}

### experience

- {"name":"8+ Years Backend Engineering at Scale","description":"Minimum 8+ years of professional backend software engineering experience with proven track record building and operating production-grade systems serving significant traffic or workloads. Recognized expertise by peers and cross-functional partners, not just general seniority."}
- {"name":"Production Infrastructure Ownership","description":"Direct experience owning mission-critical backend services through full lifecycle: design, implementation, deployment, incident response, and optimization. Track record of making architectural decisions that have withstood production scale and complexity. Experience on-call for systems you built."}
- {"name":"Systemic Problem Identification and Cross-Team Leadership","description":"Demonstrated ability to identify architectural, process, and tooling gaps across organizational boundaries and drive solutions to completion. Experience working with multiple teams, navigating organizational dynamics, and building consensus around technical direction. Examples of initiatives that improved broader engineering practices."}
- {"name":"Product-Minded Technical Leadership","description":"Experience influencing product direction, working with non-technical stakeholders, and translating business requirements into technical strategy. Track record of making judgment calls others rely on. Examples of taking initiative beyond assigned scope to improve outcomes for users or the organization."}
- {"name":"High-Throughput Service Development","description":"Hands-on experience building and scaling services designed for high-throughput, low-latency performance. Understanding of performance optimization, bottleneck identification, benchmarking, and tuning. Experience shipping systems where performance directly impacts user experience and business metrics."}
- {"name":"Mentorship and Technical Standard-Setting","description":"Track record of elevating engineering team capabilities through mentorship, code review, architecture guidance, and establishing technical standards. Examples where your influence raised the bar for code quality, architectural sophistication, or engineering rigor across the team."}

## Skills

### required

- {"name":"Go Programming Language","description":"Expert-level proficiency in Go for building production systems. Expertise in concurrency primitives (goroutines, channels), standard library, performance optimization, and Go ecosystem tooling."}
- {"name":"Rust Programming Language","description":"Expert-level proficiency in Rust for systems programming. Deep understanding of ownership model, lifetime management, trait systems, and writing high-performance, memory-safe code."}
- {"name":"Distributed Systems Design","description":"Advanced knowledge of distributed system principles including CAP theorem, consistency models, consensus protocols, fault tolerance, and scalability patterns."}
- {"name":"Cloud Platform Engineering","description":"Hands-on expertise with major cloud platforms (AWS, GCP, Azure) including networking, security, storage, and observability services. Understanding of infrastructure-as-code and cloud-native design patterns."}
- {"name":"API Design and Specification","description":"Expertise in designing clean, maintainable, scalable APIs. Understanding of REST principles, gRPC, versioning strategies, backward compatibility, and API security."}
- {"name":"System Architecture and Design","description":"Expert-level ability to design complex systems for scale, reliability, and maintainability. Understanding of tradeoffs between consistency, availability, and latency. Ability to articulate architectural decisions and their implications."}

### preferred

- {"name":"OpenAI, Anthropic LLM API Integration","description":"Hands-on experience integrating with OpenAI, Anthropic, or similar LLM provider APIs. Understanding of model selection, pricing models, rate limiting, and error handling specific to LLM services."}
- {"name":"RAG and Vector Database Architecture","description":"Familiarity with Retrieval-Augmented Generation architectures, vector databases (Pinecone, Weaviate, Milvus), and embedding systems. Understanding of how RAG patterns affect system design and performance."}
- {"name":"AI Agent Framework Experience","description":"Working knowledge of agent orchestration frameworks such as LangChain, AutoGPT, CrewAI, or similar platforms. Understanding of agent lifecycle, tool management, and state handling patterns."}
- {"name":"Open Policy Agent (OPA)","description":"Experience with Open Policy Agent or similar declarative policy engines. Understanding of how to integrate policy evaluation into data flows without performance degradation."}
- {"name":"OpenTelemetry and Observability","description":"Hands-on experience implementing observability using OpenTelemetry. Knowledge of metrics, traces, and logs integration. Experience designing observable systems from inception."}
- {"name":"Kubernetes and Container Orchestration","description":"Advanced hands-on experience with Kubernetes, including advanced networking, storage, scheduling, and security features. Experience running production Kubernetes clusters at scale."}
- {"name":"Docker and Containerization","description":"Deep understanding of containerization concepts, Docker architecture, image optimization, and container security. Valuable given Docker's container-centric platform."}
- {"name":"OAuth 2.1 and IAM Systems","description":"Detailed knowledge of OAuth 2.1 flows, OpenID Connect, and cloud identity systems. Experience implementing secure authentication in distributed systems."}
- {"name":"Secret Management and Vaulting","description":"Hands-on experience with HashiCorp Vault, AWS Secrets Manager, or similar secret management systems. Understanding of rotation, versioning, and audit logging for secrets."}
- {"name":"Microservices Architecture","description":"Proven experience designing and scaling microservices architectures. Understanding of service boundaries, inter-service communication, deployment patterns, and operational complexity management."}
- {"name":"Technical Leadership and Standard-Setting","description":"Track record of setting technical standards and patterns that were adopted across multiple teams or the broader organization. Evidence of influencing engineering culture and practices."}

## Tech stack

### tools

### others

### databases

### languages

### frameworks

## Benefits

### benefits

## Compensation

- **max:** 275550
- **min:** 170350
- **currency:** USD
- **stockOptions:** true

## Interview process

### steps

## Full description
## **About Docker**

Docker has been one of the most loved brands in developer tooling, trusted by more than 20 million monthly users and over 20 billion container image pulls. From solo founders to the world's largest companies, developers rely on Docker to build, share, and run their applications across our suite of products including Docker Desktop, Docker Hub, and Docker Scout.  
  
We are a globally distributed, remote-first team building the tools that define how software gets built and delivered. As AI agents redefine software development, Docker is at the center of that shift, providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default.

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Docker AI Gateway is our answer to the complexity of taking AI agents from prototype to production. It’s a powerful, intelligent, and secure control point that eliminates the toil of model orchestration, tool management, observability, and governance - so developers can focus on building incredible AI agents, not gluing together infrastructure.

The Gateway sits at the center of modern AI applications, offering:

* A model and tool routing layer with built-in security and cost optimization
* A familiar OpenAI-compatible interface and MCP server
* Unified observability and policy enforcement
* Auto-RAG, tool injection, session summarization, and more

We’re just getting started and we need exceptional engineers to help us build the backbone of the future of agent-based development.

## **What You’ll Do**

* Set technical direction for core systems powering the AI Gateway 0 the model router, MCP gateway, and control plane - and make sure architectural decisions hold up as usage scales by 10x
* Consistently identify gaps in our infrastructure, process, or tooling before they become blockers, and drive the fix from idea to production
* Take a significant, hands-on role in defining, delivering, and implementing the Gateway's technical strategy, in close partnership with engineering leadership
* Own the hardest, highest-ambiguity problems in the system: dynamic model selection, auto-failover, cost-based routing, and policy enforcement that has to work end-to-end
* Act as the go-to expert for secure credential storage, session summarization, caching, and rate limiting - the capabilities everything else depends on
* Build the infrastructure that lets us, and our customers, evaluate, replay, and backtest agent and LLM behavior with confidence
* Mentor senior and mid-level engineers, and raise the technical bar for the team through code, design review, and example
* Partner with product, design, and leadership to shape not just what the control plane UI does, but where the product goes next

## **Key Problems You’ll Help Solve**

* Define the unified abstraction layer across diverse model and tool providers (OpenAI, Anthropic, Google, AWS Bedrock) that the rest of the org builds on
* Architect secure, scalable identity and credential vaulting for tool and model access, trusted enough to run at enterprise scale
* Build the infrastructure for real-time and historical analytics of AI agent behavior that leadership and customers both rely on
* Guarantee that policy enforcement and logging work end-to-end - from prompt to tool to response - with no gaps an auditor (or attacker) could find
* Set the bar for developer experience through intuitive APIs and first-class observability

## **Who You Are**

**Required Qualifications**

* 8+ years of backend engineering experience, with a track record of owning production-grade systems at significant scale
* Deep specialization in distributed systems, cloud-native infrastructure, and API design - recognized by peers and cross-functional partners as a go-to expert, not just experienced
* Experience building secure, high-throughput services (gateways, proxies, load balancers, policy engines), and a history of making judgment calls other engineers rely on
* Fluency in Go and/or Rust
* A track record of identifying systemic gaps - in architecture, process, or tooling-— and driving the fix across team boundaries, not just within your own
* A strong product mindset, with real experience shaping technical direction rather than only executing against it
* Comfortable operating with wide latitude: given a problem and significant autonomy in how to solve it, including when and who to pull in
* Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience

## **Bonus Qualifications**

* Prior experience with OpenAI, Anthropic, or similar LLM APIs
* Familiarity with RAG architectures, vector databases, or agent frameworks (e.g., LangChain, AutoGPT, CrewAI)
* Experience with policy engines (e.g., OPA), observability frameworks (e.g., OpenTelemetry), or API gateways
* Understanding of OAuth 2.1, secret management, and cloud IAM systems
* Experience with Kubernetes, Docker, and microservices architecture
* A track record of setting technical standards that other teams or engineers went on to adopt

## Why Join Us?

* **Work on what’s next:** The agent era is just beginning. Help define the infrastructure stack for the next generation of software.
* **Own mission-critical infrastructure:** Be part of a small team building the backbone of secure, intelligent agent applications.
* **Impact at scale:** Your work will touch tens of thousands of developers building AI-first applications.
* **Engineering excellence:** Join a world-class engineering team that values simplicity, code quality, and deep technical thinking.
* **Remote-first culture:** Work from anywhere with a team that trusts and empowers you.

**Docker considers sponsorship on a case-by-case basis based on business needs.**

**Compensation & Equity**

**United States:** $170,350 – $275,550 + equity

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## **Posting Information**

* **Open vacancy:** This posting is for an existing open role.
* **AI in hiring:** Docker may use AI-assisted tools during our recruiting process.
* **Interview recordings:** Candidates will be invited to opt in to interview recordings to support interviewer calibration and consistent evaluations. Recordings are optional and require explicit consent.

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## **Perks & Benefits**

* **Remote-first by design** – Work from your home, with offices in Seattle and Paris for connection and collaboration.
* **Flexibility that fits your life** – We trust you to manage your schedule while delivering great work.
* **Time to recharge** – Generous PTO, designated quarterly Whaleness Days, and a designated end-of-year Whaleness break.
* **Home office support** – Set up your workspace for comfort and success.
* **Technology stipend** – Equivalent to **US$100 net per month** to help support your work.
* **Learning & development** – Annual stipend for conferences, courses, certifications, and continued learning.
* **Parental leave** – 16 weeks of paid parental leave after six months of employment.
* **Equity for all full-time employees** – Share in Docker's long-term success as we continue to grow.
* **Comprehensive benefits** – Medical, retirement, and paid holidays vary by country.
* **Docker swag** – Because representing the whale never gets old.

_Docker is proud to be an equal opportunity employer. We are committed to building a team that reflects a broad range of backgrounds, experiences, and perspectives. We believe diverse teams build better products, make better decisions, and better serve our global community._

#LI-REMOTE