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About This Role
We are seeking an experienced SaaS Project Manager with enterprise AI deployment experience to help build and scale a newly formed AI & Data organization.
As the first external hire on the team, you'll help bring structure, governance, and visibility to more than 40 active and emerging AI initiatives across the business. Working closely with internal stakeholders, executive leadership, and the VP of AI & Data, you'll lead intake, prioritization, portfolio reporting, and project delivery coordination while helping establish how AI initiatives are evaluated, governed, and delivered across the company. The role is focused exclusively on internal stakeholders and enterprise AI initiatives.
Success in this role requires experience working in a SaaS environment, where priorities evolve quickly, multiple initiatives compete for attention, and strong cross-functional collaboration is critical. You'll thrive if you enjoy building processes, creating structure from ambiguity, and driving initiatives forward in a fast-moving organization.
Before You Apply This is an individual contributor project management role focused on coordinating, prioritizing, governing, and delivering AI initiatives across the business. This is not a software engineering, machine learning, data science, AI architecture, or AI model development position.
What You'll Do
- Manage multiple concurrent AI projects and initiatives ranging from proof-of-concepts to long-term governance programs.
- Own project intake, demand management, evaluation, prioritization, documentation, tracking, and portfolio reporting.
- Partner with internal stakeholders to collect business requirements, define use cases, establish priorities, and secure alignment on project outcomes.
- Build and maintain executive dashboards and reporting that provide visibility into project progress, value, timelines, dependencies, and priorities.
- Conduct gap analyses to identify current-state challenges, future-state opportunities, and expected business outcomes.
- Facilitate build-versus-buy evaluations and support business case development.
- Coordinate cross-functional technology initiatives across departments such as cybersecurity, infrastructure, HR, operations, revenue teams, and other business functions.
- Support enterprise AI governance, security, data protection, adoption, and change management efforts.
- Identify process improvement opportunities, application overlap, and opportunities for consolidation or optimization.
- Establish scalable processes that enable the AI & Data organization to effectively manage a growing portfolio of enterprise AI initiatives.
Why Join Us?
- Be one of the first hires in a newly formed AI & Data organization.
- Help shape how enterprise AI initiatives are evaluated, prioritized, governed, and delivered.
- Work directly with executive leadership and key stakeholders across the company.
- Influence AI adoption across a wide range of business functions, including HR, cybersecurity, infrastructure, operations, and revenue-generating teams.
- Help create order, visibility, and execution discipline across a rapidly growing portfolio of AI initiatives.
- Join at an exciting stage of the company's AI journey where meaningful impact can be made from day one.
Requirements
- 5+ years of project or program management experience, including significant experience working within a SaaS organization.
- Experience navigating cross-functional technology initiatives within a SaaS business environment where priorities and stakeholder needs evolve rapidly.
- Demonstrated experience managing multiple concurrent projects or programs simultaneously.
- Experience owning project intake, demand management, prioritization, portfolio tracking, executive reporting, and dashboard development.
- Strong executive communication skills with the ability to summarize complex topics, communicate business value, and influence decision-making.
- Experience delivering technology initiatives across multiple business and technical functions.
- Enterprise AI deployment experience, including exposure to AI governance, security, risk, or data protection considerations.
- Experience with enterprise AI platforms such as Microsoft Copilot, Claude, OpenAI, Anthropic, GPT, or similar large language model technologies.
- Experience participating in build-versus-buy technology evaluations and business case discussions.
- Ability to work independently with minimal direction and thrive in a newly forming organization.
- Strong problem-solving skills, initiative, and a "get things done" mindset.
- Comfort operating in ambiguity while helping build new processes and operating models.
Preferred Qualifications* Experience facilitating organizational change management efforts.
- PMP, PMI-ACP, Six Sigma, or related certifications.
- Experience working in a high-growth SaaS environment.
- Experience supporting globally distributed teams and stakeholders.
- Demonstrated use of AI tools to improve personal productivity, project management workflows, and business outcomes.
Benefits
At HSI, we believe great work happens when people are supported, trusted, and empowered to grow.
- Fully Remote Work: Work where you're most productive—we trust you.
- Unlimited PTO: Recharge when you need to—we support work-life balance.
- Career Growth: Unlimited access to our Learning & Development library, including our own HSI LMS content.
- Well-Being First: Full medical, dental, vision, and mental health coverage.
- Secure Your Future: Generous $1:$1 401(k) company match—your future is important to us.
- Compensation: This position has an annual salary range of $100,000-$125,000. Placement within the range will depend on experience, qualifications, geographic location, and other business considerations.
Application Process To ensure a fair and consistent hiring process, all candidates must apply through our online application system.
Due to the volume of applications we receive, we are unable to respond to individual inquiries regarding application status or consideration for the role. Candidates whose qualifications closely match our requirements will be contacted directly by our recruiting team.
Salary Context
This $100K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At HSI, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($110K) sits 48% below the category median. Disclosed range: $100K to $120K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
HSI AI Hiring
HSI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $120K - $120K.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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