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About This Role
Why Join Ivo?
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Every civilization runs on the same infrastructure: agreements between people who don't fully trust each other. Sumerians pressed them into clay. Romans carved them into stone. We bury them in 80\-page PDFs.
The way those agreements are reviewed hasn't changed in four thousand years \- a human reads the whole thing and tries not to miss anything. We're building the AI that finally changes that. Ivo is the contract intelligence platform of choice for companies like Uber, Meta, Canva, IBM, and Shopify. We recently raised our Series B and have grown 800% over the last 12 months.
Engineering at Ivo
======================
Engineers at Ivo are inventors. Ivo was first\-to\-market with:
- An AI agent that lives in MS word and edits the document for you \[2023]
- Ditching imprecise embeddings models in favor of agentic RAG \[2023]
- Large\-scale LLM\-based legal fact extraction \[2024]
- A legal assistant that can search large contract databases, without sacrificing accuracy \[2024]
- Clustering legal documents descended from the same family \[2025]
- Automatic deviation analysis to locate buried risk in huge contract databases \[2025]
- Merging contracts with their amendments to make a time series of “composite” contracts (a customer actually cried when we showed her this) \[2025]
The Role: Why, What and the Who
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*Why*? Infrastructure Engineers build the foundation for Ivo’s entire platform. Customers are cagey about their contracts, so each customer gets their isolated environment with containers, database, VPC, etc. Things break. Regions go down. Cloud and LLM providers have “incidents.” Customers still expect us to hit our SLAs.
*What*? We’re looking for a Cloud/MLOps Engineer as part of Infrastructure team to:
- Own and evolve our Kubernetes platform across AWS/GCP/Azure
- Design and operate multi\-cluster / multi\-region architectures with failover and disaster recovery strategies adhering to secure cluster isolation boundaries
- Build internal tooling for cluster provisioning and lifecycle management, standardizing environments (dev staging prod)
- Design strategies to isolate ML vs API workloads while optimizing for cost, performance, and reliability
- Implement security and compliance controls at the platform layer with RBAC, workload identity, secrets management while preserving data isolation aligned with residency requirements and auditability for enterprise customers
- Partner with SRE \+ ML teams to ensure SLOs are realistic and enforceable and models are deployed in production environments
*Who*? We need someone who has:
- Minimum 7 years of experience
- Deep, hands\-on experience with Kubernetes in production (you’ve debugged it at 2am, not just deployed to it)
- Strong experience with infrastructure as code (Pulumi, Terraform, etc.)
- Strong understanding of cluster architecture, scheduling, networking, storage primitives and failure modes in distributed systems
- Experience managing multi\-cluster or multi\-region setups with Github CI/CD
This isn’t a “keep the lights on” role. You’ll be building the system that keeps the company running. In addition to helping us run a solid, high\-performance distributed system, we’d love someone who’s as excited about LLMs as we are. You’d be deeply embedded into the engineering team and highly encouraged to push the frontiers.
Ivo might be a good fit for you if you:
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- You love writing code, but you love having impact more: We’re a team of engineers at heart, but our \#1 goal is building the best possible product. That means making pragmatic choices and looking for 80/20 solutions.
- Would describe yourself as being relentlessly resourceful.
- You have a strong internal sense of urgency. You have a bias towards doing things \*today\*, rather than tomorrow.
- Experience working in a startup environment is preferred but not required.
- Are excited about the adventure of building a company!
What We Offer
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- Competitive Compensation: Final offer details are determined based on experience, expertise, and overall fit.
- Equity: Meaningful ownership in a company that's scaling fast
- Relocation and Visa Support: We also offer relocation assistance for successful applicants moving to SF, as well as support for visa and green card applications where applicable.
- Health \& Wellness: Comprehensive medical, dental, and vision plans to suit the needs of you and your family.
- Flexible Spending \& Insurance: Access to HSA and FSA accounts, plus life insurance coverage.
- 401(k) Program: Save for the future with our 401(k) program.
- Commuter Benefits: We help make getting to and from the office easier and more convenient.
- Unlimited PTO: So you can take the time you need to recharge, stay healthy, and bring your best self to work.
- Office Perks: Enjoy a vibrant Downtown San Francisco office with catered lunch five days a week, premium snacks and coffee, an in\-building gym, and a dog\-friendly environment.
FAQ
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- *What stage of growth is Ivo at?:* We launched in early access in 2023\. Since then, we’ve had an incredible response from the market and are growing rapidly. We 6x'd in ARR in the last 12 months. Our clients include companies like Uber, Reddit, IBM, Canva, Pinterest, WordPress, and more. We're happy to share more details with candidates who go through our interview process.
- *Is this a chill gig?:* Startups are very hard, especially if they’re growing fast. You’ll have a ton of responsibility, and there’s always an enormous amount of stuff to do. It’s hard work but the payoff is uncapped.
- *Can I work remotely?:* We require candidates to work with us in\-person 5 days a week in our San Francisco office.
*Ivo is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.*
Compensation Range: $287K \- $485K
Salary Context
This $287K-$485K range is above the 75th percentile for MLOps Engineer roles in our dataset (median: $168K across 34 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 4,317 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At ivo, 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 $203,000 based on 85 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($386K) sits 90% above the category median. Disclosed range: $287K to $485K.
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.
ivo AI Hiring
ivo has 1 open AI role right now. They're hiring across MLOps Engineer. Based in San Francisco, CA, US. Compensation range: $485K - $485K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national 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 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).
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 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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