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About This Role
Position overview
Client is investing in enterprise AI capabilities to accelerate delivery, enable self-service technology, and help teams solve meaningful business problems faster. Tulip is a strategic AI platform that provides reusable patterns, tools, and services to make AI-enabled solution delivery easier, safer, and more scalable across the organization. Increasingly, Tulip reaches the business through desktop applications used every day by corporate, support center, and operations teams across Mac and Windows.
The Senior AI Desktop Engineer - Tulip Platform is a hands-on role responsible for designing, building, and shipping cross-platform desktop applications - built with Electron and running on both macOS and Windows - that put Tulip's AI capabilities directly into people's daily tools. The focus is user experience and workflow automation: replacing manual, fragmented, and browser-tab-sprawl processes with fast, native-feeling desktop workflows, deeply integrated with enterprise systems and secured through enterprise SSO and identity. The role also contributes to platform architecture, integration patterns, and engineering standards, and mentors peers through design discussion and code review.
The ideal candidate is a pragmatic builder and strong technical partner who obsesses over the end-user experience, holds desktop software to a native-quality bar on both macOS and Windows, navigates ambiguity, and consistently delivers high-quality, AI-enabled desktop solutions aligned to business priorities.
Job Qualifications
8+ years of professional software engineering experience, including significant hands-on experience building and shipping production cross-platform desktop applications with Electron (or comparable frameworks) on both macOS and Windows.
Demonstrated focus on user experience: partnering with design, prototyping and testing with real users, and shipping interfaces that are fast, accessible, and intuitive for non-technical corporate and operations staff.
Experience automating manual and fragmented workflows into desktop-first digital processes, including local caching and sync, background and long-running tasks, native OS notifications, deep links and protocol handlers, file system and clipboard integration, and multi-window experiences.
Experience integrating AI-enabled services into end-user applications, including large language models, retrieval-augmented generation, agent or workflow orchestration, conversational and voice interfaces, and responsible AI guardrails at the point of use.
Strong API and integration skills: consuming and shaping REST, GraphQL, and event-driven services, and working with backend and platform engineers to make desktop clients performant and resilient.
Experience with desktop release and quality practices: code signing and macOS notarization, auto-update channels, enterprise distribution through Intune, Jamf, or comparable MDM, CI/CD for multi-platform builds, crash and performance monitoring, automated UI testing, and phased rollouts.
Working proficiency in Python or a comparable backend language, with enough platform and cloud fluency (Azure, AWS, or GCP; containers; CI/CD; observability) to extend Tulip services where the desktop experience requires it.
Experience securing desktop and enterprise integrations, including OAuth 2.0 and OpenID Connect, enterprise SSO on managed Mac and Windows endpoints, local data protection using the OS keychain and credential vault, and secrets management.
Strong communication skills, with the ability to work directly with corporate and operations teams to observe how work actually gets done and translate that into technical designs and executable engineering work.
Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field preferred, or equivalent practical experience.
Preferred Qualifications
Experience building desktop or internal productivity applications for corporate, support center, finance, or operations teams in complex enterprise environments.
Experience with design systems, accessibility standards, usability testing, and instrumenting product analytics to measure adoption and task completion.
Familiarity with Azure AI, Azure OpenAI, Microsoft Fabric, vector search, prompt orchestration, or agentic AI frameworks, and with enterprise AI governance and responsible AI controls.
Experience integrating desktop applications with enterprise systems such as Microsoft 365, ERP, ticketing, HRIS, data platforms, or identity providers.
Experience working in retail, restaurant, ecommerce, loyalty, finance, supply chain, or high-volume enterprise environments.
Experience partnering with product managers, designers, business analysts, forward deployed engineers, and enterprise architects to move ideas from user discovery to delivery.
Skills
Cross-platform desktop application engineering with Electron on macOS and Windows
User experience design partnership, prototyping, usability testing, and accessibility
Workflow automation and digitization of manual, fragmented, and browser-bound processes
Native-feeling desktop UX, OS integration, notifications, and performance and memory tuning
API, integration, and eventing patterns across enterprise and AI services
LLM, RAG, agentic workflows, and AI evaluation applied to end-user experiences
Python, SQL, cloud-native engineering, and secure service design
Desktop CI/CD, code signing and notarization, auto-update and release management, crash and performance monitoring, and automated testing
User discovery, stakeholder communication, and translating operational needs into product
Peer mentorship, engineering standards, design reviews, and code quality
Key Result Areas (KRAs)
These KRAs describe the primary outcomes expected from this role and how success will be evaluated.
Desktop Experience and Product Delivery
Design, build, and ship desktop applications on Tulip that corporate, support center, and operations teams find fast, intuitive, and worth using every day.
Example Measures: Adoption and active usage, task completion time, user satisfaction, crash-free sessions
Workflow Automation on the Desktop
Replace manual, fragmented, and browser-tab-sprawl steps with automated desktop workflows, using AI capabilities such as orchestration, RAG, agents, and conversational interfaces where they add real value.
Example Measures: Processes automated, hours returned to teams, error and rework reduction
Integration and Platform Contribution
Integrate desktop applications with Tulip services and enterprise systems, and extend platform capabilities where the experience requires it.
Example Measures: Integration reliability, reusable client patterns, delivery predictability
Engineering Excellence
Apply and improve practices for secure, maintainable, testable, and observable desktop and service engineering, including release management and phased rollouts.
Example Measures: Code quality, test coverage, release stability, defect reduction
Engineering Practices and Knowledge Sharing
Contribute to documentation, design reviews, and shared UI and integration patterns that strengthen how the team builds desktop AI solutions.
Example Measures: Improved team clarity, stronger handoffs, repeatable delivery practices
Business Alignment and Value Realization
Spend time with corporate and operations teams, translate what you observe into product decisions, and tie delivered experiences to measurable business value.
Example Measures: User feedback loops, stakeholder alignment, business impact tracking
Ways of Working
This role is a hands-on individual contributor with no people management responsibility. The Senior AI Desktop Engineer ships user-facing applications, contributes to technical decisions, supports roadmap execution through their own delivery, mentors less experienced engineers, and models practices that improve quality, velocity, and consistency. Success requires deep technical execution paired with genuine curiosity about how corporate and operations teams work, and clear communication across product, design, architecture, engineering, and business partners.
Location Requirement
Location expectations should be confirmed through the People and Talent process. Draft assumption: Tempe, Arizona / hybrid, aligned to client's current Technology in-office guidance.
Physical Requirements / Work Environment
This position operates in a professional office or remote work environment and routinely uses standard office equipment. It also includes periodic time with teams across clients support center and office locations to observe workflows, test desktop applications in live conditions, and gather user feedback, which may involve occasional travel. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Compensation, Benefits and Duration
Minimum Compensation: USD 56,000
Maximum Compensation: USD 196,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is available for independent contractors
No applications will be considered if received more than 120 days after the date of this post.
Salary Context
This $56K-$196K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Photon, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($126K) sits 41% below the category median. Disclosed range: $56K to $196K.
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.
Photon AI Hiring
Photon has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span Sunnyvale, CA, US. Compensation range: $175K - $196K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 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).
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 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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