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About This Role
GenOS is Intuit's Generative AI Operating System — the platform every GenAI experience at Intuit is built, deployed, and governed on, including the customer\-facing Intuit Assist. The stack:
· GenStudio — LLM sandbox and extensible model catalog; new models onboarded in days.
· AI Workbench — versioned Prompt Management and the LLM Leaderboard for benchmarking.
· GenRuntime — GenOrchestrator (planner, executor, memory, retrieval) plus agents and tools grounding LLMs in Intuit domain knowledge.
· GenUX — 140\+ AI UX components consumed by product teams.
· GenSRF — security, risk, and fraud guardrails as a platform feature, not an afterthought.
Multi\-LLM catalog: Anthropic Claude via AWS Bedrock, Gemini, Llama, and Mistral.
The Problem This Role Exists to Solve
Apps built through GenOS ship with an AI\-accelerated development innerloop — and no generated quality assets. Development is agentic end\-to\-end; QE starts from zero, manually, after the fact. Testing is now the bottleneck consuming the platform's velocity gains. This role makes QE a native GenOS output: build an app on the platform, and its test agents, suites, and release gates come with it — self\-serve, on the paved road, like every other capability.
Role Summary
Senior, expert\-level, embedded hands\-on engineer building QE enablement as a GenRuntime platform capability. You build the agents and platform hooks that automatically emit test assets and quality gates for every app scaffolded through GenOS. You ship production code weekly under Intuit lead direction. This is a build role — the deliverable is platform capability, not test execution, not advisory, not architecture\-on\-slides.
Key Responsibilities
· Build QE\-generation agents on GenRuntime: agents that consume artifacts the GenOS dev flow already produces — requirements, code diffs, API specs, GenUX component usage — and emit functional, API, and regression test assets as validated, structured output.
· Wire QE enablement into the paved road: an app scaffolded through GenOS gets QE agents attached by default — no opt\-in ceremony, self\-serve onboarding, zero hand\-holding.
· Build the agent toolbelt as reusable GenRuntime tools: test execution, self\-healing selectors and API contracts, failure triage, defect summarization — including agent\-to\-agent patterns where test agents interrogate the application's own agents.
· Extend AI Workbench eval primitives (LLM Leaderboard, prompt evaluation) into release gates for GenAI applications: golden datasets, LLM\-as\-judge scoring, statistical quality thresholds enforced in paved\-road CI/CD — not just model selection.
· Embed GenSRF coverage into generated tests: safety, privacy, and moderation regressions become executable test cases, not audit findings.
· Integrate with Feature Management so QE\-agent rollout is flagged, measured, and adoption\-tracked per product team.
· Write well\-tested, production\-grade code; participate in reviews and design discussions — PR merge velocity and AI\-assisted code in PRs are tracked org KPIs.
· Participate in the production support/on\-call rotation for the QE capability surface (rotation shape and compensation treatment per Open Items).
· Contribute self\-serve onboarding docs and inner\-source repos — inner\-source contribution is a tracked PDX metric.
Must\-Have Qualifications
· 7\+ years backend/platform engineering (Java, Python, or Go) with deep distributed\-systems fundamentals: async processing, caching, idempotency, failure handling.
· 2–3\+ years building LLM\-powered systems in production, not prototypes: agent/orchestration frameworks (LangChain / LlamaIndex / homegrown), structured tool calling, prompt versioning and evaluation.
· Structured\-output engineering at production grade — generated tests are code artifacts: schema enforcement, output validation, and repair loops are the daily job.
· Demonstrated QE domain depth: has built or owned test automation architecture — framework design, CI quality gates, flaky\-test economics — enough to encode that judgment into agent behavior. A platform engineer who has never owned a test suite will build QE agents that generate garbage confidently.
· One major LLM provider at scale — AWS Bedrock strongly preferred — with real operational scars: rate limits, latency variance, provider failover, version drift.
· AWS \+ Kubernetes deployment depth (services run on Intuit Kubernetes Service).
· Fluent in AI\-assisted development workflows — Copilot\-class tooling is the expected daily working mode.
Nice\-to\-Have
· LLM evaluation engineering: golden datasets, LLM\-as\-judge calibration, mutation testing or fault injection to validate generated\-test quality.
· MCP tool integrations; SSE/WebSocket streaming for agentic responses.
· Vector stores and retrieval in production (OpenSearch / pgvector / Pinecone or equivalent).
· Multi\-tenant enterprise platforms under strict security/compliance; fintech background.
· Open\-source / inner\-source contribution record.
Pay: $75\.00 \- $85\.00 per hour
Benefits:
- Relocation assistance
Work Location: In person
Salary Context
This $156K-$176K range is below the median 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 Bintech Group, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($166K) sits 23% below the category median. Disclosed range: $156K to $176K.
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.
Bintech Group AI Hiring
Bintech Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Mountain View, CA, US. Compensation range: $176K - $176K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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