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About This Role
Our Mission:
6sense's mission is to multiply what matters: growth, retention, and efficiency. We envision a future where companies, teams and people reach their full potential.
Our People:
People are the heart and soul of 6sense. We serve with passion and purpose. We live by our Being 6sense values of Win as One Team, Stay Curious, Do The Right Thing, Own the Outcome, and Create Belonging. Every 6sensor plays a part in defining the future of our industry\-leading technology. 6sense is a place where difference\-makers roll up their sleeves, take risks, act with integrity, and measure success by the value we create for our customers. We want 6sense to be the best chapter of your career.
Job Title
(Sr) Director, IT and AI Transformation
Organizational Reporting
CISO
### Purpose of the Job
This role leads the evolution of Business Technology from traditional IT operations into an automation\-first, AI\-enabled internal platform. The Sr. Director owns end\-to\-end employee technology experiences, spanning service desk, endpoint management, corporate infrastructure, and a new Enterprise AI function responsible for building and operating AI systems, workflows, and agents.
Success in this role means reducing manual work, accelerating employee productivity, and embedding AI into how the company operates day to day while maintaining strong reliability, security, and compliance.
Responsibilities \& Accountabilities
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### 1\. Enterprise AI Platform \& Automation
- Own the Enterprise AI strategy and delivery, including internal copilots, agents, and workflow automation
- Build and operate AI\-powered internal tools (e.g., support automation, knowledge retrieval, workflow orchestration)
- Drive adoption of AI across functions (GTM, Engineering, G\&A)
- Establish governance for AI usage, model risk, data access, and safe deployment along
- Partner with Security on AI risk management (data leakage, prompt injection, model misuse)
- Standardize tooling for prompt management, orchestration layers, and agent frameworks
- Define and track KPIs such as:
+ % of workflows automated
+ Ticket deflection via AI
+ Employee productivity gains
### 2\. IT Operations \& Employee Experience
- Own global IT service delivery, including:
+ Service Desk (L1–L3 support, self\-service, automation\-first)
+ Endpoint and asset lifecycle management (laptops)
+ Identity\-adjacent user lifecycle workflows (joiner/mover/leaver in partnership with IAM/Security/People)
- Deliver a consumer\-grade employee experience with measurable SLAs and NPS (Net Promoter Score)
### 3\. Corporate Infrastructure \& SaaS Enablement
- Own baseline corporate technology stack including:
+ Collaboration tools (Zoom, Slack), productivity suites (M365\), idp (Okta), endpoint tools (MDM)
+ Corporate internet and Zoom rooms for 6sense offices
- Ensure systems are:
+ Highly reliable and scalable
+ Optimized for a remote\-first workforce
- Partner cross\-functionally to enable fast, secure SaaS adoption
### 4\. Automation, AIOps, and Operational Excellence
- Drive an "automation\-first" operating model across IT
- Implement AIOps capabilities to:
+ Reduce incidents
+ Predict failures
+ Automate remediation
- Eliminate repetitive manual work across IT and adjacent teams
- Establish clear operational metrics, including:
+ MTTR (Mean Time to Resolution)
+ Ticket deflection rate
+ Automation coverage
+ Cost per employee supported
### 5\. Security \& Compliance Partnership
- Partner closely with Security to ensure:
+ Compliance with SOC 2, ISO 27001, ISO 42001, and internal controls
+ Secure deployment of IT and AI systems
- Integrate security by design into IT and AI platforms
- Support audits and evidence collection through automation
### 6\. Vendor \& Financial Management
- Own strategic vendor relationships across IT and AI tooling
- Optimize cost, utilization, and contract value
- Rationalize overlapping tools and drive platform consolidation
Performance Measurement/KPIs
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- % reduction in manual IT work through automation
- AI adoption across internal workflows and teams
- Employee experience (NPS, ticket resolution time, self\-service rates)
- Reliability and performance of IT systems
- Audit and compliance outcomes
- Cost efficiency per employee
Person Specification
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Experience
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- 10\+ years in IT, with 5\+ years leading global, high\-performing teams
- Experience operating in a cloud\-native SaaS environment
- Proven track record of driving automation and operational transformation
- Deep understanding of:
+ IT Service Management (ITSM / ITIL)
+ Endpoint and asset management
+ SaaS and corporate infrastructure environments
- Strong partnership with Security on compliance and audit programs
### Preferred
- Experience building or leading Enterprise AI or internal AI platforms
- Familiarity with:
+ LLMs (large language models)
+ AI agents and orchestration frameworks
+ Retrieval\-based systems and prompt engineering
- Experience implementing AIOps or intelligent automation platforms
Competencies and Behaviors
------------------------------
- Automation mindset: defaults to eliminating work, not scaling it
- Strong operator with ability to balance speed, scale, and control
- Clear communicator across technical and non\-technical audiences
- High ownership and bias for action in ambiguous environments
- Builds high\-performing, adaptable teams across IT and AI disciplines
- Experience with business transformation
Base Salary Range: $203,916\.75 \- $275,077\.90\. The base salary range represents the anticipated low and high end of the base salary range for this position. Actual salaries may vary and may be above or below the range based on various factors, including but not limited to work location and experience. The base salary is one component of 6sense's total compensation package for this position. Other compensation may include a bonus program or commission plan, and stock options if approved by 6sense's board. In addition, 6sense provides a variety of benefits, including generous health insurance coverage, life, and disability insurance, a 401K employer matching program, paid holidays, self\-care days, and paid time off (PTO). \#Li\-remote
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Our Benefits:
Full\-time employees can take advantage of health coverage, paid parental leave, generous paid time\-off and holidays, quarterly self\-care days off, and stock options. We'll make sure you have the equipment and support you need to work and connect with your teams, at home or in one of our offices.
We have a growth mindset culture that is represented in all that we do, from onboarding through to numerous learning and development initiatives including access to our LinkedIn Learning platform. Employee well\-being is also top of mind for us. We host quarterly wellness education sessions to encourage self care and personal growth. From wellness days to ERG\-hosted events, we celebrate and energize all 6sense employees and their backgrounds.
Equal Opportunity Employer:
6sense is an Equal Employment Opportunity and Affirmative Action Employers. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, or disability status. If you require reasonable accommodation in completing this application, interviewing, completing any pre\-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to [email protected].
*We are aware of recruiting impersonation* *attempts* *that are not affiliated with 6sense in any way.* *All email communications from* *6sense* *will originate from* *the @6sense.com domain.* *We will* *not initially contact you via text message and will* *never request payments.* *If you are uncertain whether you have been contacted by an official 6sense employee, reach out to* *jobs@6sense.com*
Salary Context
This $203K-$275K range is above the 75th percentile 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 6sense, 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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($239K) sits 29% above the category median. Disclosed range: $203K to $275K.
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.
6sense AI Hiring
6sense has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $260K - $275K.
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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