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About This Role
#### \#TeamNextdoor
Nextdoor (NYSE: NXDR) is the essential neighborhood network. Neighbors, public agencies, and businesses use Nextdoor to connect around local information that matters in more than 350,000 neighborhoods across 11 countries. Nextdoor builds innovative technology to foster local community, share important news, and create neighborhood connections at scale. Download the app and join the neighborhood at nextdoor.com.
#### Meet Your Future Neighbors
As a Client Platform Engineer, you will connect IT and Engineering for AI tools and automation at Nextdoor. As more teams start using AI agents, assistants, and connectors, you will manage the IT platforms that keep these tools safe and scalable. This includes handling identity and access for non\-human and user\-delegated agent identities in Okta, overseeing governance and reviews for new agents and MCP connectors, tracking usage and adoption, and managing costs and licenses to keep our AI use sustainable. You will work with teams in Business Technology, Engineering, Security, and Finance to create processes that help teams launch AI tools quickly while following IT guidelines. Your work will help make AI a valuable resource across the company.
At Nextdoor, we operate in an AI\-first environment and expect every team member to actively use AI tools as part of their workflow. We aren't looking for prompt engineers; we're looking for people who use tools like Claude, Gemini, ChatGPT, and Glean to challenge their own thinking and take full ownership of AI\-assisted outputs.
We also offer a warm and inclusive work environment that embraces a hybrid employment model, blending an in office presence and work from home experience for our valued employees. The hiring team will go over these expectations with you if you are being considered for a role near one of our offices in San Francisco, Los Angeles, Chicago, Dallas, New York, and London.
#### TheImpact You'll Make
If you have experience making IT platforms reliable, secure, and easy to use, and you want to apply those skills to AI tools across the business, this role could be a great fit. You will set the standards for how every AI agent at Nextdoor authenticates, what it can access, what it costs, and how teams are alerted if issues arise. You will be responsible for the standards, review processes, and monitoring that turn one\-off AI projects into a dependable platform for the company.
Your responsibilities will include:
- Design and operate the identity, access, and credential lifecycle for AI agents at Nextdoor, including non\-human and user\-delegated identities in Okta, OAuth 2\.0, and OIDC scope minimization, and canonical authentication patterns (user OAuth, service accounts, workload identity federation) that teams default to alongside Nextdoor's existing Network and AV Standards
- Build and run review processes for AI agents and connectors, covering pre\-production review of agent tool surfaces, data scopes, and blast radius; intake review for new MCP servers and third\-party connectors (vendor diligence, OAuth scope approval, hosted vs. self\-hosted decisions); and the underlying policy framework for registration, data classification, and human\-in\-the\-loop requirements
- Maintain a centralized registry of AI agents and tooling deployed across Nextdoor, and own the associated telemetry, including Datadog dashboards, vendor analytics ingestion (e.g., the Anthropic Enterprise Analytics API), and recurring department\-level adoption reporting for IT and executive leadership
- Own seat governance and cost controls across Nextdoor's AI tool portfolio (Claude Enterprise, Cursor, Copilot, and emerging tools), including per\-user and per\-org spend caps, multi\-tenant license allocation across vendor organizations, quarterly access reconciliation against Okta, and monthly budget reviews for IT and Finance
- Participate in in\-person Nextdoor events such as trainings, off\-sites, volunteer days, and team building exercises
- Build in\-person relationships with team members and contribute to Nextdoor's company culture
#### What You'll Bring To The Team
- 5\+ years in IT engineering, client platform engineering, or IAM\-focused security engineering, with clear ownership of production identity and access systems. And/or the ability to perform at an advanced level in the domain
- Deep hands\-on Okta administration experience, including SSO/SAML/OIDC, SCIM, group rules, sign\-on policies, and access certification, with a strong understanding of audit and SOX implications
- Working knowledge of OAuth 2\.0, OIDC, and service account or workload identity patterns across Google Cloud and AWS, including the ability to spot anti\-patterns (e.g., domain\-wide delegation, over\-scoped service account keys) before they reach production
- Comfortable writing scripts to automate IT operations in Python, Bash, or similar, and experience operating CI/CD or scheduled\-job patterns (GitHub Actions, cron) for IT automation.
- Hands\-on experience with AI development tools (Claude, GitHub Copilot, LangChain, etc.) as a practitioner, not just an administrator
- Familiarity with at least one observability platform, with Datadog preferred, including building dashboards, alerts, and ingestion pipelines from third\-party APIs
- Strong written communication and the ability to author internal standards, runbooks, and review documentation that other teams will actually use
Preferred Qualifications
- Direct experience with Claude Enterprise, GitHub Copilot, Cursor, or comparable AI tooling deployed at enterprise scale, including license and seat governance
- Working knowledge of MCP (Model Context Protocol), agent integration patterns, or comparable agent\-to\-tool connector frameworks
- Background working alongside Engineering on shared platform tooling, with the ability to operate at the boundary of IT and Engineering ownership
- Experience with Jamf Pro and macOS endpoint management
- Prior involvement in SOX access certification or quarterly UAR processes
Bonus Points
- Experience evaluating SaaS vendors and MCP or connector marketplaces for security posture, data flow, and supply\-chain risk
- Experience with AI governance frameworks (NIST AI RMF, ISO 42001, EU AI Act)
- Familiarity with Workato, Tray.io, Okta Workflows, or other iPaaS platforms
- IT certifications (Okta, Jamf, Google Cloud, AWS, or similar)
#### Rewards
Compensation, benefits, perks, and recognition programs at Nextdoor come together to create our total rewards package. Compensation will vary depending on your relevant skills, experience, and qualifications.
The starting salary for this role is expected to range from $140,000\-$150,000 on an annualized basis, or potentially greater in the event that your 'level' of proficiency exceeds the level expected for the role. The salary range will be determined by the candidate's geographic location.
We expect to award a meaningful equity grant for this role. With quarterly vesting, your first vest date will take place within 3 months of your start date.
When it comes to benefits, we have you covered! Nextdoor employees can choose between a variety of health plans, including a 100% covered employee only plan option, and we also provide a OneMedical membership for concierge care.
At Nextdoor, we empower our employees to build stronger local communities. To create a platform where all feel welcome, we want our workforce to reflect the diversity of the neighbors we serve. We encourage everyone interested in our mission to apply. We do not discriminate on the basis of race, gender, religion, sexual orientation, age, or any other trait that unfairly targets a group of people. In accordance with the San Francisco Fair Chance Ordinance, we always consider qualified applicants with arrest and conviction records.
For information about our collection and use of applicants' personal information, please see Nextdoor's Personnel Privacy Notice, found here.
Salary Context
This $140K-$150K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Nextdoor, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($145K) sits 20% below the category median. Disclosed range: $140K to $150K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Nextdoor AI Hiring
Nextdoor has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $150K - $150K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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