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About This Role
Overview
Intuit's Data, Growth & Experiences (DGX) organization is building the trusted, AI-ready data foundation that powers Intuit's System of Intelligence — fueling personalized customer experiences, GTM growth, and agent-ready workflows across the company. We're looking for a Manager 3, AI Science (MLOps, AI, Data Science) to lead a multi-team AI Science organization within DGX, spanning three interconnected areas: MarTech (the intelligent, connected capabilities that power customer acquisition, engagement, and retention at scale); Data — across data definition, governance, processing, persistence, query, semantic modeling, and analytics, as we transform Intuit's data ecosystem into a governed, AI-ready knowledge stack anchored by a shared semantic layer; and Data Acquisition (IDX) — the pipelines that connect and standardize data flowing in from third-party systems, making it consumable, agent-actionable, and ready for activation without bespoke integration work.
This role owns technical direction across these capabilities on a 1–3 year horizon, translating DGX's data and AI strategy into production-grade, agent-native architectures. You will lead a team of Data Scientists and AI Scientists building the models, pipelines, and semantic infrastructure that let Intuit's products, GTM teams, and AI agents reason over trusted, governed, real-time data — partnering closely with Product and Engineering leaders to embed AI and agent-driven development into how DGX builds. This is a people-management role with significant technical scope: you're expected to be fairly hands on to shape the science, the data architecture, and platform strategy across all of DGX from an AI science focus and adoption, not just manage delivery.
Responsibilities
Technical & AI/Platform Strategy
- Shape technical direction across DGX capabilities, aligning architecture and roadmaps into cohesive, AI-ready systems that advance DGX's Contextual Knowledge Stack and Business Object Model goals.
- Own the science behind a self-learning semantic layer — infrastructure that encodes business rules and organizational context as reusable knowledge, reconciles fragmented data across various sources in capabilities and products, and verifies its own outputs before they reach a decision-maker or an agent, in the spirit of self-learning cognitive data layers.
- Design verification and eval layers so semantic outputs (entity resolution, metric definitions, business context) are checked before reaching downstream agents or systems, and build feedback loops so definitions improve over time rather than going stale
- Provide leadership in the development of digital-twin models for marketing and sales — audience segments, product lineups, and quote/pricing structures — enabling scenario simulation ahead of product decisions, continuously recalibrated against real transactional, behavioral, and performance data
- Drive anomaly detection as a AI science speciality across the data estate: data quality (missing values, duplicates, distribution shifts), lineage (silent pipeline breaks, drift in 3rd-party IDX feeds), and context/semantic anomalies (inconsistent term usage signaling Business Object Model drift) — surfacing issues before they propagate into GTM decisions or agent actions
- Drive adoption of Intuit's AI/GenAI paved roads and agent skills across integrated systems; embed AI and agent-driven development into products, platforms, and processes, and contribute improvements back to shared platforms
- Guide the team's approach to evaluating and optimizing agentic AI systems — including eval design for non-deterministic, multi-step flows, and judgment on when to shift from prompting to fine-tuning to reinforcement learning
Governance & Product Partnership
- Lead practices for coding, testing, resiliency, security, and compliance across complex, multi-capability systems, including AI/agent guardrails; partner with Directors, Architects, and Program peers on systemic risk
- Partner deeply with Product and Design to understand customer problems and co-create AI-native solutions, including designing tests and shaping how AI surfaces in the product experience
- Partner with Product, Design, and Customer Success to drive experimentation, validate solutions, and scale what delivers lasting impact
Team Leadership & Culture
- Scale leadership and talent systems across teams by empowering managers and tech leads through effective delegation; partner with HR, Product, and Engineering to build a culture of excellence, collaboration, and growth
- Set a high-performance bar with disciplined management; build a strong leadership pipeline and mentor senior ICs and managers to lead AI-native teams effectively
- Build strategies for skills development, mentorship, succession planning, and feedback across the AI Science craft (Algorithmic Modeling, MLOps, Product Sense)
Execution & Operational Discipline
- Lead execution across multiple teams and capabilities, translating strategy into scalable delivery systems
- Align cross-team priorities by weighing business impact, technical health, and innovation; coach leads to strengthen planning
- Drive integrated processes across a complex ecosystem, using data and insights to improve reliability, efficiency, and throughput
Qualifications
Required
- 10+ years of experience in AI/ML, Data Science, or MLOps, including demonstrated technical depth in at least one core area (algorithmic modeling, production ML systems, or applied data science)
- 3+ years of people management experience, including managing principals or senior/staff-level ICs across multiple teams
- Multiple years of experience in applying advanced analytics techniques such as python, ML models, LLMs, etc., is highly preferred.
- Proven experience shaping technical strategy and architecture for AI/ML systems at scale — from data curation through production deployment
- Strong hands-on background in ML Operations: building and operating production-grade models, designing data pipelines, and establishing engineering standards for AI assets
- Experience with semantic layers, knowledge graphs, or entity-resolution systems — building or operating infrastructure that encodes business context and rules as reusable, queryable knowledge rather than static documentation
- Experience building statistical or ML-based anomaly detection for production data systems — data quality, pipeline/lineage monitoring, or drift detection
- Experience partnering cross-functionally with Product, Design, and Engineering to ship AI-driven customer experiences
- Bachelor's degree in Computer Science, Statistics, Data Science, or a related quantitative field; Other applied fields like Cognitive Sciences, Psychology, Economics in combination with advanced degrees in one of the quantitative fields or AI; Master's or PhD preferred
- Strong proficiency in Python and ML frameworks; working knowledge of cloud ML infrastructure (AWS, GCP, Databricks)
Preferred
- Experience with generative AI, LLM-based systems, or agentic AI architectures, including evaluation design for non-deterministic, multi-step systems
- Experience with reinforcement learning, fine-tuning, or prompt optimization techniques and knowing when to apply each
- Experience building simulation or digital-twin models (e.g., audience/segment modeling, pricing/quote simulation, or scenario planning) for marketing, sales, or GTM use cases
- Experience with self-learning or continuously-updating data systems — infrastructure that improves its own definitions, mappings, or verification logic from usage feedback rather than requiring manual upkeep
- Experience with third-party data integration and standardization — normalizing external data (CRM, ERP, ad platforms, etc.) into internal data models at scale
- Track record of building and scaling AI Science or Data Science teams within a large, matrixed technology organization
- Experience driving org-wide platform adoption (shared frameworks, paved roads) to reduce duplication and increase engineering efficiency
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
The expected base pay range for this position is:
Mountain View $264,500 - $357,500
Salary Context
This $264K-$357K range is above the 75th percentile for MLOps Engineer roles in our dataset (median: $157K across 20 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 9,653 AI roles we're tracking, MLOps Engineer positions make up 2% of the market. At Intuit, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $205,000 based on 151 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($311K) sits 52% above the category median. Disclosed range: $264K to $357K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Intuit AI Hiring
Intuit has 32 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer, MLOps Engineer. Positions span Mountain View, CA, US, San Diego, CA, US, New York, NY. Compensation range: $116K - $369K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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