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About Us:
We love going to work and think you should too. Our team is dedicated to trust, customer obsession, agility, and striving to be better everyday. These values serve as the foundation of our culture, guiding our actions and driving us towards excellence. We foster a culture of performance and recognition, allowing us to transform growth as we enable our employees to do the best work of their careers.
This role is open to candidates based in or near San Francisco, CA. At LogicMonitor, we hire within our Centers of Energy—vibrant locations where our teams connect, collaborate, and innovate.
To learn more about life at LogicMonitor, check out our Careers Page.
What You'll Do:
LogicMonitor® is the AI\-first hybrid observability platform powering the next generation of digital infrastructure. LogicMonitor delivers complete visibility and actionable intelligence across on\-premises, cloud, and edge environments. By anticipating issues before they strike, optimizing resources in real time, and enabling faster, smarter decisions, LogicMonitor helps IT and business leaders protect margins, accelerate innovation, and deliver exceptional digital experiences without compromise.
Our customers love LogicMonitor's ability to bring cloud and traditional IT together into one view, as seen in minimal churn rates, expansion business, and exciting new customer references. In fact, LogicMonitor has received the highest Net Promoter Score of any IT Infrastructure Management provider. LogicMonitor also boasts high employee satisfaction. We have been certified as a Great Place To Work®, and named one of BuiltIn's Best Places to Work for the seventh year in a row!
As Regional Vice President, Sales for Edwin AI, you will own and scale the global go\-to\-market motion for Edwin AI, leading a team of specialized Account Executives focused on high\-growth, AI\-driven revenue expansion. This is a mission\-critical leadership role responsible for building and executing a co\-prime sales motion alongside core LogicMonitor sales, driving aggressive pipeline generation, deal execution, and revenue outcomes.
You will define the sales strategy, operating model, and execution rigor required to establish Edwin AI as a category\-defining solution in AIOps and intelligent operations. This role requires a leader who can operate at both strategic and tactical levels—shaping market positioning, enabling the field, and directly engaging in complex enterprise deals.
You will partner closely with Product, Marketing, Customer Success, and Pre\-Sales to ensure strong alignment between market demand, product capabilities, and customer outcomes, while building a high\-performance, globally distributed sales team.
Here's a closer look at this key role:
Sales Strategy \& Execution
- Own global revenue targets for Edwin AI, including pipeline generation, bookings, and expansion across new and existing customers
- Define and execute a co\-prime sales motion with core Account Executives, ensuring clear roles, accountability, and alignment on deal ownership
- Drive aggressive growth targets by building a predictable, scalable pipeline and disciplined forecasting model
- Personally engage in strategic enterprise deals, guiding deal strategy, executive alignment, and closing execution
Team Leadership \& Development
- Build, lead, and scale a high\-performing global team of Edwin AI Account Executives
- Establish clear performance expectations, coaching frameworks, and operating cadence to drive consistent overachievement
- Recruit top\-tier sales talent with experience in enterprise AI, SaaS, and complex solution selling
- Foster a culture of accountability, urgency, and customer\-centric selling
Go\-to\-Market \& Field Enablement
- Define the Edwin AI sales playbook, including qualification criteria, sales stages, and value\-based selling methodologies
- Partner with Marketing to refine positioning, messaging, and demand generation strategies
- Enable the broader sales organization on Edwin AI use cases, value propositions, and competitive differentiation
- Drive field readiness through training, tools, and repeatable sales motions
Cross\-Functional Collaboration
- Partner with Product and Engineering to provide market feedback, influence roadmap priorities, and ensure alignment with customer needs
- Collaborate with Pre\-Sales and Customer Success to ensure strong technical validation and successful customer outcomes
- Work closely with RevOps to optimize pipeline visibility, forecasting accuracy, and performance metrics
Operational Excellence
- Establish KPIs and dashboards to track pipeline health, conversion rates, deal velocity, and revenue performance
- Implement scalable processes for territory planning, account targeting, and pipeline management
- Continuously refine sales strategies based on data, market trends, and competitive insights
What You'll Need:
- Bachelor's degree or equivalent experience in Business, Technology, or a related field
- 12\+ years of experience in enterprise software sales, with at least 5\+ years in sales leadership roles
- Proven track record of building and scaling high\-performing sales teams in high\-growth environments
- Experience selling AI, AIOps, observability, or enterprise SaaS solutions to large enterprises
- Demonstrated success in managing complex, multi\-stakeholder enterprise sales cycles
- Strong understanding of value\-based selling and solution\-oriented sales approaches
- Experience operating in a co\-sell or overlay sales model
- Exceptional leadership, communication, and executive presence
- Ability to operate in a fast\-paced, ambiguous environment with aggressive growth expectations
*Residents of California, click* *Here* *to view our California Applicant Privacy Notice.*
*Anticipated Application Close Date: 07**/20/26*
*LogicMonitor is an Equal Opportunity Employer*
*At LogicMonitor, we believe that innovation thrives when every voice is heard and each individual is empowered to bring their unique perspective. We're committed to creating a workplace where diversity is celebrated, and all employees feel inspired and supported to contribute their best.*
*For us, equal opportunity means fostering a truly inclusive culture where everyone has the chance to grow and succeed. We don't just open doors; we invite you to step through and be part of something bigger. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.*
*Work Authorization:*
*At this time, we are able to consider candidates who are authorized to work in the United States on a full\-time, permanent basis without requiring new or initial employer\-sponsored work authorization.*
*Candidates who currently hold valid U.S. work authorization that can be transferred to a new employer (such as certain H\-1B statuses) may be considered on a case\-by\-case basis.*
*We are not able to provide new sponsorship for employment\-based visas that require an initial petition or application by the employer.*
*Notice Regarding Use of AI in Hiring*
*We use artificial intelligence tools to assist with reviewing job applications, such as matching skills and experience to job requirements. These tools support, but do not replace, human review. All hiring decisions are made by our recruiting and hiring teams.* *You may opt out of AI processing at any time, and your application will still be reviewed. To opt out, please contact us at* *[email protected]**.*
*By submitting your application, you acknowledge this notice.*
###### \#LI\-JP1 \#LI\-Hybrid \#BI\-Hybrid
Our goal is to ensure an accessible and inclusive experience for every candidate.
If you need a reasonable accommodation during the application or interview process under applicable local law, please submit a request via this Accommodation Request Form.
Know your rights: workplace discrimination is illegal. Please click here to review LogicMonitor's U.S. Pay Transparency Nondiscrimination Provision.
Salary Context
This $132K-$217K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At LogicMonitor, 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 $185,000 based on 13,200 positions with disclosed compensation. This role's midpoint ($175K) sits 5% below the category median. Disclosed range: $132K to $217K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
LogicMonitor AI Hiring
LogicMonitor has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $217K - $217K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,258 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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