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Senior Member of Technical Staff, AI Quality
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Harper is an AI\-native commercial insurance company in San Francisco. We're not bolting AI onto insurance — we're rebuilding the entire business as software, on a simple bet: turning expert human judgment into compute is one of the largest transitions left to make, and a trillion\-dollar industry still run 90% by hand is the place to prove it. We've grown \~100x in the last year and we move at that speed — on\-site, in person, long days, very high standards. Almost no one joins Harper *for insurance*; they join to build the company that replaces how it works.
The role
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Turning judgment into compute only compounds if the company can tell whether the compute is getting better. Today that's mostly vibes: an engineer ships a prompt change, a tool change, or a new model and judges it by feel — "seems better," "the demo passed." Vibes don't survive Series B, and they definitely don't survive an agent that's quoting real coverage for real businesses. Your job is to turn agent quality from a vibe into a number. Harper's agents handle intake, sales, service, voice, and submission packaging; every one needs to be evaluated, regression\-tested, and monitored in production. You'll work alongside the engineer setting AI\-quality direction and own a specific agent surface end\-to\-end — so that when the agent improves we know, and when it regresses we know before the customer does. That's how we scale judgment without scaling headcount.
What you'll do
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- Build capability \+ regression eval suites for your assigned agents — intake, submissions, placements, renewals, CRM, or voice.
- Curate golden datasets from real failure modes: real transcripts, real underwriter back\-and\-forth, real call recordings. 20–50 sharp cases per agent, not thousands of synthetic ones.
- Design graders. Deterministic first (string match, state check, tool\-call assertions); LLM\-as\-judge where deterministic fails; human calibration on samples.
- Ship pre\-merge eval gates. Every PR touching an agent, prompt, or tool runs the relevant suite in CI. Below threshold, it's blocked.
- Wire production trajectory monitoring. Online evaluators score live trajectories; drift gets caught within hours.
- Turn ops findings into permanent tests. Every flagged failure becomes a regression case; every repeat issue becomes a test that catches it forever.
What we're looking for
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- 3–6 years building software, with hands\-on production LLM/agent eval experience — capability \+ regression suite design, LLM\-as\-judge graders, golden datasets.
- You can describe a specific regression an eval suite you built caught — and exactly how it would have leaked otherwise.
- You've designed an LLM\-as\-judge rubric that survived human calibration, and you debug a hallucination by reading transcripts, not aggregate dashboards.
- Familiar with at least one major eval framework; strong written communication (rubric docs, failure\-mode taxonomies).
- You write code with AI daily and have real opinions on which agent behaviors actually matter.
- Bonus: open\-source eval\-framework contributions; red\-team/adversarial testing; voice eval (latency, interruption, transcription accuracy); ML eval/observability background.
The reality
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On\-site in San Francisco, in person, long days, high standards. AI quality is the discipline that decides whether the whole bet holds, which means the work is scrutinized and the bar is high — your evals are what let everyone else ship fast without flying blind. The right person wants that leverage and that pace.
Logistics
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- Compensation (OTE): $176,000–$253,000 cash (base \+ target performance bonus), plus competitive equity.
- Location: San Francisco, in\-office. Based here or willing to relocate.
- Benefits: Uber commuter benefits; breakfast, lunch, and dinner provided; snacks and coffee stocked; free gym membership; health, dental, and vision.
- Process: Founder call (15 min) Tech Lead deep\-dive (60 min, eval architecture and real failure modes) Super Day on\-site founder \+ Tech Lead offer. No committee. Best offer, first.
To apply: If you've turned vibes into a number — built an eval suite that caught a regression a model upgrade silently introduced — send your resume, the framework, and a transcript of a failure you found that nobody else did.
Compensation Range: $176K \- $253K
Salary Context
This $176K-$253K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2064 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 3,963 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Harper, 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 in Demand for This Role
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 $180,000 based on 12,398 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($214K) sits 19% above the category median. Disclosed range: $176K to $253K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Harper AI Hiring
Harper has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $253K - $308K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,103 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 3,963 open positions tracked in our dataset. By seniority: 116 entry-level, 1,875 mid-level, 1,532 senior, and 440 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (593 positions). The remaining 3,349 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 3,963 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,783), Data Scientist (297), 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 (116) are outnumbered by mid-level (1,875) and senior (1,532) 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 440 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (593 positions), with 3,349 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 $200,000. Top-quartile roles start at $253,000, 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 $290,000 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 (2,043 postings), Aws (1,241 postings), Azure (934 postings), Rag (886 postings), Gcp (774 postings), Pytorch (614 postings), Prompt Engineering (614 postings), Claude (564 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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