---
title: 'Staff Network Engineer, App Platform at Scale AI'
canonical: 'https://feeny.ai/job/staff-network-engineer-app-platform-scale-ai-san-francisco-g4a2tkk4bwdf'
type: 'job'
last_seen: '2026-09-09'
---

# Staff Network Engineer, App Platform at Scale AI

- **Company:** Scale AI
- **Location:** San Francisco, CA
- **Compensation:** $1/hr
- **Posted:** 2026-09-03
- **Last confirmed live:** 2026-09-09
- **Apply:** https://job-boards.greenhouse.io/scaleai/jobs/4730337005

## Job description

Scale GP (Scale Generative AI Platform) is Scale’s enterprise AI platform, providing APIs and infrastructure for knowledge retrieval, inference, evaluation, agents, and more. We deploy SGP across AWS, Azure, and GCP, often directly into customer-controlled cloud environments across highly regulated industries including healthcare, financial services, telecom, and retail.

As SGP has grown in scale and complexity, networking has become a critical architectural discipline of its own. Today, teams routinely encounter the same hard problems — VPC design, routing, ingress and egress, private connectivity, service exposure, address-space constraints, firewall policies, and cross-environment communication — but solve them differently depending on the deployment. We’re looking for a Senior Network Engineer to establish the architectural standards for how SGP connects to customer infrastructure and how traffic moves throughout the platform.

You’ll own network architecture across a large and rapidly growing fleet of Kubernetes environments spanning AWS, Azure, and GCP, with many deployed inside customer-owned cloud accounts and networks that we do not control. The constraints vary significantly: a commercial deployment behind a customer-managed API gateway; a hub-and-spoke enterprise network where the customer assigns our address space; a GovCloud environment protected by default-deny firewall policies; or a fully air-gapped deployment with no external connectivity.

The goal is not to create a bespoke network architecture for every customer. Your job is to define one clear, secure, and supportable networking model for SGP — and establish the small set of defensible variations required to operate across different clouds, customer architectures, and compliance regimes.

## What You'll Do

- Own network architecture for the SGP platform: define and enforce network standards across cloud environments (AWS, Azure, GCP) and customer deployments
- Design and review VPC architectures, peering, DNS, load balancing, and CDN/edge configurations (e.g., Cloudflare), grounding root-cause and tradeoff discussions in data and domain depth
- Bring strong technical judgment to VPN, routing, access, ingress/egress, and traffic-flow decisions
- Review proposed networking solutions from platform, product, and forward-deployed teams; evaluate what should and should not be introduced into the platform boundary
- Define the standard connectivity pattern between Scale's control plane and customer data planes (VPC peering, PrivateLink/Private Service Connect, site-to-site VPN, reverse tunnels) — and converge today's per-customer designs onto it
- Own the customer-boundary delivery blueprint: how code, images, and traffic cross into a customer tenant — CI/CD mirroring across org boundaries (including customer-side Azure DevOps), artifact scan gates, private registries, ingress — so new engagements configure a pattern instead of designing one
- Own engineer access into customer and internal environments (Tailscale/Teleport/bastion-class decisions), replacing per-engineer VPN sprawl and hand-rolled tunnels with something auditable
- Own the Kubernetes traffic layer: Istio/Envoy mesh and ingress, the per-cloud CNI matrix, and a portable NetworkPolicy contract that constrains egress for thousands of short-lived agent-sandbox pods running untrusted code
- Reduce rework by preventing one-off implementations from becoming long-term platform burden
- Partner with security engineering on network segmentation, zero-trust access, and compliance requirements in regulated customer environments
- Debug complex connectivity, latency, and traffic-flow issues across hybrid and multi-cloud deployments
- Document network architecture, standards, and runbooks so adjacent teams can operate confidently

## What We're Looking For

- 5+ years of network engineering experience, including designing and operating production networks in cloud environments
- Deep expertise in cloud networking on at least two of AWS, Azure, and GCP: VPC design, peering, Transit Gateway/hub-and-spoke topologies, private connectivity (PrivateLink, Private Service Connect), and DNS
- Kubernetes networking depth — CNI, ingress, NetworkPolicy, service mesh (Istio/Envoy) — this is where most of our real incidents live
- Strong fundamentals in TCP/IP, BGP, routing, firewalls, VPN (site-to-site and client), and TLS
- Experience with edge/CDN and traffic-management platforms such as Cloudflare
- Has defined a network standard or reference architecture that other teams adopted, and enforced it through design review
- Comfortable as the sole domain owner: able to collect requirements across five-plus live environments you didn't design, then converge them without breaking any
- Has made build-vs-adopt networking calls with vendor-support or contractual consequences in a customer's cloud
- Infrastructure-as-code proficiency (Terraform preferred)
- Familiarity with network security and compliance requirements in regulated industries (healthcare, finance, government), including environments across multiple compliance regimes (commercial, FedRAMP/GovCloud, air-gapped), is a plus
- Excellent communication skills — able to explain networking tradeoffs to both technical and non-technical audiences and influence decisions without direct authority

## Why This Role Matters

Without a dedicated owner, adjacent teams are covering a domain that needs specialized expertise. You will be the point of accountability for network architecture as SGP grows — raising the quality of every deployment, unblocking confident decisions, and keeping the platform boundary clean as we scale. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is: $1—$1 USD

PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

About Us:

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.

We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.

We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's [Know Your Rights poster](https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf) for additional information.

We comply with the United States Department of Labor's Pay Transparency provision.

PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our [privacy policy](https://scale.com/legal/privacy) for additional information.

## About Scale AI

## Company Overview
- **One-liner**: Scale AI provides high-quality data, RLHF, model evaluations, and full-stack AI infrastructure to help enterprises and governments build, deploy, and oversee reliable AI systems.
- **Entity Type**: Private (49% non‑voting stake owned by Meta Platforms as of June 2025; total funding $1.6B across eight rounds)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2016
- **Founders**: Alexandr Wang, Lucy Guo (Wang left in June 2025; current CEO is Jason Droege)

## Core Business
- **Primary industry**: AI infrastructure, data annotation, large language model (LLM) evaluation, enterprise AI deployment
- **Target customers**: B2B – Enterprise and government organizations; also serves leading AI labs (e.g., OpenAI, Google DeepMind, Meta, Microsoft, General Motors)
- **Mission**: “Develop reliable AI systems for the world's most important decisions.”

## Products & Services
- **Data at Scale**: High-quality training data, annotations, and RLHF for advanced AI models (SaaS + human-in-the-loop service)
- **Evaluations**: Rigorous model evaluations, benchmarking, and red‑teaming to measure and improve AI performance (service)
- **Applied AI**: Full‑stack AI systems that help enterprises and governments build, deploy, and oversee reliable AI (SaaS + consulting)
- **Safety, Evaluation and Alignment Lab**: Research arm focused on LLM alignment and safety (internal R&D; also co‑created the “Humanity's Last Exam” benchmark)
- **Subsidiaries**: Remotasks (computer vision and autonomous vehicle data labeling), Outlier (LLM data annotation)

## Market Standing
- **Valuation**: $29B (as of 2025 – cited on scale.com)
- **Key Metric**: Total Funding – $1.6B (including the $14B Meta investment that acquired a 49% non‑voting stake in June 2025)
- **Notable Investors/Partners**: Meta Platforms (49% owner), with commercial customers including Google, Microsoft, Meta, General Motors, OpenAI, and Time. Also works with U.S. and Qatari governments.
- **Growth Signals**:
  - Headcount: Scale.com cites “1,000+” employees; LinkedIn reports 3,751 employees (+32.1% YoY).
  - 90% of the world’s leading generative AI model builders are powered by Scale.
  - 15 billion human decisions used to train AI models; $1 billion paid to contributors globally.
  - Active job postings: 284+ (as of July 2025), with strong hiring in enterprise engineering, AI agents, and solutions roles.

## Competitive Advantages
- **Data moat**: Scale’s proprietary Data Engine and access to millions of human‑annotated decisions create high‑quality training data that competitors cannot easily replicate.
- **Trust & adoption**: Used by 90% of leading GenAI builders; runs private benchmarks for the most ambitious AI companies.
- **Government credibility**: Direct contracts with the U.S. Department of Defense and international governments (e.g., Qatar) – a high‑barrier entry point.
- **Full‑stack offering**: From raw data annotation to LLM evaluation and end‑to‑end applied AI deployment, Scale covers the entire AI lifecycle.
- **Research leadership**: In‑house Safety, Evaluation and Alignment Lab; co‑creator of the “Humanity's Last Exam” benchmark.

## Strategic Focus
- **Enterprise GenAI agents**: Ramping up “AgentOps” and “Frontier Agents” engineering teams (many open roles in SF, NY, London, Budapest).
- **Healthcare & life sciences**: Hiring dedicated GTM leaders and AI strategists for healthcare vertical.
- **International expansion**: Growing offices in London, Budapest, Mexico City, and Washington DC.
- **Safety and alignment**: Continued investment in red‑teaming, model evaluations, and government‑focused AI safety contracts.

## Why Work Here
- **Mission‑driven**: “Develop reliable AI systems for the world’s most important decisions” – directly shaping frontier AI capabilities.
- **Compensation & benefits**: Comprehensive health, dental, vision, mental health services; generous PTO; annual learning & development stipend; parental leave; ERGs; guest‑friendly offices with happy hours, game nights, book clubs.
- **Engineering culture**: Credos such as “Write the Market,” “Find the 20%,” “Earn Customer Love,” and “Quality is Our Cheat Code” emphasize impact over effort, customer obsession, and structured thinking.
- **Growth trajectory**: Rapid headcount growth (+32%), major Meta investment, and expansion into new verticals signal a company in high‑growth mode.
- **Flexible work**: Offices in SF, NY, London, Budapest, Mexico City, DC; the careers page highlights “flexible environment,” though specific remote/hybrid policy is not explicitly stated.

## Sources
1. [scale.com](https://scale.com/about)
2. [scale.com](https://scale.com/)
3. [scale.com/careers](https://scale.com/careers)
4. [linkedin.com](https://www.linkedin.com/company/scaleai)
5. [greenhouse.io](https://job-boards.greenhouse.io/scaleai)

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