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About This Role
CaptivateIQ is transforming the way companies plan, manage, and optimize sales performance. We started by revolutionizing incentive compensation management, and now we're expanding our platform to solve broader sales planning challenges. Recognized by industry analysts like Forrester and G2 and backed by top\-tier investors, including Sequoia, ICONIQ, Accel, and Sapphire Ventures, we empower high\-growth companies like Netflix, Figma, and Stripe with the flexibility and insights needed to drive revenue performance.
About the Role
CaptivateIQ is building AI capabilities that will transform how enterprises manage sales performance. We're looking for a \*\*Staff Software Engineer\*\* to set the technical strategy for our Agentic SDK team, an internal developer platform that enables product teams across the company to build AI\-powered features.
This is a strategic technical leadership role with multi\-year, multi\-team scope. You'll define the architecture and technical direction for AI at CaptivateIQ, making decisions that have no clear answer and partnering with senior Engineering, Product, and Design leadership to shape our long\-term vision. You'll be responsible for technical choices that affect the entire organization, from platform design to engineering\-wide quality standards.
As the technical anchor on a small, high\-impact team, you'll lead by influence across organizational boundaries. You'll drive alignment between product teams consuming the Agentic SDK and the platform capabilities we build. You'll establish the bar for engineering excellence and invest deeply in coaching P4 engineers toward staff\-level impact.
This is a rare greenfield opportunity to define how AI gets built at a growing enterprise software company. You'll shape not just the technology, but the culture and practices that scale with the organization.
Job LocationRemote \- US
### Responsibilities
- Set the multi\-year technical strategy for AI platform development, partnering with senior EPD leadership on long\-term vision
- Architect foundational AI systems that serve multiple product teams, including LLM orchestration frameworks, MCP infrastructure, and agent patterns
- Own technical decisions with organization\-wide impact where the right answer is ambiguous or contested
- Define engineering\-wide quality standards and best practices for AI development, establishing patterns that scale across teams
- Drive technical alignment across the Agentic SDK team and product teams consuming AI capabilities
- Invest deeply in coaching P4 engineers, helping them develop toward staff\-level scope and strategic thinking
- Represent CaptivateIQ's technical perspective in industry discussions, open\-source contributions, or technical publications
### Requirements
- 8\+ years of professional software engineering experience with demonstrated progression into staff\-level technical leadership
- Deep expertise in LLM orchestration: production experience building agent frameworks, including agent design patterns, tool integration, and workflow optimization
- Significant experience designing MCP integrations and knowledge systems at scale, including tool server architecture, embedding pipelines, and context optimization
- Track record of setting technical strategy that spans multiple teams and multi\-year time horizons
- Experience partnering with senior leadership (Directors, VPs) to align technical direction with business objectives
- Demonstrated ability to make high\-stakes technical decisions under extreme ambiguity
- Strong mentorship track record, particularly in developing senior engineers toward staff\-level impact
- Demonstrated curiosity and continuous learning in the rapidly evolving AI/LLM space
### Bonus
- Strong proficiency in Python and experience with Django or similar backend frameworks
- Full\-stack capabilities with React and TypeScript
- Experience building and scaling internal AI/ML platforms or developer experience infrastructure
- Familiarity with LLM orchestration frameworks (LangChain, LangGraph, or equivalent)
- Deep expertise in advanced agent architectures: multi\-agent coordination, autonomous planning, or complex tool ecosystems
- Experience with AI evaluation, observability, and production monitoring at enterprise scale
- Published technical writing, conference talks, or significant open\-source contributions in AI/ML
- Background building AI\-powered products that shipped to enterprise customers with measurable business impact
- Experience at a B2B SaaS company during periods of significant growth or platform expansion
- Familiarity with sales performance management, incentive compensation, or adjacent enterprise domains
### Benefits
- Comprehensive Healthcare: 100% coverage for medical, dental, and vision for all FTEs, with roughly 75% coverage for dependents.
- Flexible Time Off: Flexible vacation days plus quarterly mental health days to ensure you have the space to recharge.
- Annual Stipends: Dedicated funds for your professional development and caretaking needs.
- Work Anniversary Bonuses: Annual bonuses to celebrate your milestones and contributions to the CaptivateIQ team that grow as your tenure does.
- Retirement Savings (US\-Only): A 401(k) plan to help you invest in and secure your future.
- Premium Tools: The latest Apple hardware to empower you to do your best work.
- Inclusive Community: Active Employee Resource Groups (ERGs) that celebrate shared identities and support our DEI goals by fostering an environment where diverse talent thrives.
### Notice to Prospective Candidates
- Only emails from @captivateiq.com should be trusted.
- We are aware of active recruitment scams using the CaptivateIQ name, in which individuals pose as our recruiters and post fake remote job openings and make fake job offers on the Internet. Please note, we will never do the following:Attempt to correspond with a candidate using a free web\-based account, such as an email address that ends in @gmail.com, @yahoo.com, @hotmail.com, etc.
- Make an offer of employment without conducting multiple rounds of interviews face\-to\-face using secure video\-conferencing technology.
- Ask candidates to cash checks to buy equipment on behalf of CaptivateIQ.
- Ask candidates to make a payment in order to be considered for a position.
- Make early requests for candidates' personal information such as date of birth, passport details, credit card numbers, bank details and social security number, etc.
- Please note that we’ll only ask for more sensitive personal information in connection with background checks after an offer is made.
- Participate in an on\-call rotation to provide after\-hours support, ensuring timely resolution of critical issues and maintaining system uptime.
The base range represents the minimum and maximum for this position across North America. For candidates in Toronto, Canada the range is $181,289–$242,106\. The compensation offered for this position will depend on numerous factors, including individual proficiency, anticipated performance, and the location of the selected candidate. Our OTE is just one component of CaptivateIQ's competitive total rewards package.*CaptivateIQ participates in E\-Verify, web\-based system that allows enrolled employers to confirm the eligibility of their employees to work in the United States*
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $181K-$285K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $185K across 231 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At CaptivateIQ, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($233K) sits 7% above the category median. Disclosed range: $181K to $285K.
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.
CaptivateIQ AI Hiring
CaptivateIQ has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Remote, US. Compensation range: $285K - $285K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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