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About This Role
At Archive, we believe great products should live many lives. We build the technology that powers branded resale for some of the world's most beloved companies, including Lululemon, The North Face, New Balance, Dr. Martens, Peloton, and 50\+ others. Our platform makes it easy for brands to keep products in circulation and for customers to shop secondhand with confidence.
The secondhand market is growing three times faster than traditional retail and is projected to reach $350B globally by 2028\. We're building the infrastructure behind that shift, helping brands turn resale into a meaningful part of their business and helping consumers rethink how they shop. If you're excited to change consumer behavior for the better, we'd love to meet you.
About the role…
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Archive is hiring a CX Operations \& AI Enablement Specialist to own the systems, automation, and AI layer that powers our Customer Operations org, spanning customer support, trust \& safety, partner experience, and 3PL operations. Today, that entire layer, our AI triage and routing system, Zendesk administration, integrations, and CX reporting, is built and maintained by one person. You'll take full ownership of it: keeping it running, debugging it when it breaks, and building the next generation of automation that lets the team operate leaner as we scale.
This is a builder's role for someone who is equal parts systems thinker and scrappy operator, comfortable diagnosing a broken triage flow one hour and shipping a new automation the next, with whatever tools and workarounds get the job done. If you're fluent in Zendesk, energized by AI\-driven automation, and want to be the person who takes a CX operation from mostly\-manual to mostly\-automated, this is for you.
Responsibilities
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AI triage, routing \& agents
- Own and evolve the AI triage system that classifies and routes every inbound ticket, plus the library of AI specialist agents covering returns, order status, warranty and repair, delivered\-not\-received, and brand\-specific policies
- Build new agents and tune existing ones to take on new issue types or adjust tone and behavior to maintain brand voice
- Update ticket routing and classification logic as policies and brands change
- Build AI QA agents that review tickets against defined criteria
Production debugging \& reliability
- Resolve live issues such as duplicate triage executions, 400 errors on agent handoffs, double public replies, and tag\-matching failures
- Be the first responder when something in Zendesk breaks, from routing failures to reporting gaps
- Investigate system\-adjacent issues as they come up, such as carrier\-scan delays or dispute bugs
Zendesk administration \& systems
- Own fields, views, Tags,Triggers, macros, and configuration that keep routing and reporting accurate
- Align WFM routing between scheduling and the omnichannel queue engine, including ticket forecasting and adherence/utilization tracking
- Build and maintain the Zendesk MCP server, API keys, and OAuth permissions
- Manage account creation, deletion, and permissions
Integrations \& measurement
- Build and maintain the integrations connecting Zendesk to internal systems
- Track containment and deflection through tag\-based analysis, and diagnose root cause when rates drop
- Build and maintain Zendesk Explore reporting so managers have reliable, self\-serve data rather than building it ad hoc
Roadmap \& continuous improvement
- Expand automation coverage into ticket types the agents don't handle yet, to push containment higher
- Audit and clean up the trigger, automation, and macro library, which accumulates silent conflicts and bloat over time
- Refine classification and triage accuracy in the weak spots
- Build proper monitoring and QA on the AI agents so failures surface proactively instead of showing up as customer complaints
- Instrument fraud, dispute, and abuse signals into the AI agents so Zendesk feeds Trust \& Safety detection work
Requirements
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- Hands\-on Zendesk administration and development experience: triggers, automations, custom fields, and APIs
- Experience building AI or LLM\-driven automation, ideally in a support, classification, or routing context
- Working knowledge of customer service workflows and issues, so the agents you build actually reflect what customers are going through
- Scripting and integration skills, such as TypeScript or JavaScript, plus familiarity with REST APIs and webhooks
- Comfort operating without a playbook; this role is being defined as it's built, and the scope will shift as priorities do
- High agency, low ego, high EQ; independent and self\-directed, with the judgment to own a production system with little internal precedent to lean on
- Real curiosity about and fluency in AI; someone who tracks the pace of change rather than working from what they learned six months ago
- Comfortable moving fast and building scrappy, imperfect solutions when that's what the moment calls for, rather than waiting for the fully correct build
- Startup experience, or the equivalent comfort with ambiguity and shifting scope
Nice to have
- SQL and experience with a BI or analytics tool
- Additional coding languages beyond the must\-haves above
The expected annual base salary range for this position is 75,000 to 100,000, USD. Compensation varies based on a variety of factors which include (but aren't limited to) such as role level, skills and competencies, qualifications, knowledge, location, and experience. In addition to base pay, certain roles are eligible for equity as well, and all employees are eligible for a full benefits package including employee and dependent healthcare and 401(k) enrollment.
Archive is a Series B company backed by Lightspeed Venture Partners, Energize Capital, and Bain Capital Ventures, and was named Fast Company's \#2 Most Innovative Company in Retail in 2024\. We're a small team with low egos and high agency, working on a problem that actually matters\- and having a lot of fun doing it.
This is a remote role open across the continental US. We have offices in New York and the SF Bay Area, with hybrid options for those nearby.
*We consider applicants of all backgrounds. If you are excited about what we're building but don't meet some of the criteria above, please don't let that discourage you from applying. Please note that we are unable to accept applications from candidates outside of the US at this time.*
Compensation Range: $75K \- $100K
Salary Context
This $75K-$100K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Archive, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($87K) sits 59% below the category median. Disclosed range: $75K to $100K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Archive AI Hiring
Archive has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $100K - $100K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
Frequently Asked Questions
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