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About This Role
About Us
Our leading SaaS\-based Global Employment Platform™ enables clients to expand into over 180 countries quickly and efficiently, without the complexities of establishing local entities. At G\-P, we're dedicated to breaking down barriers to global business and creating opportunities for everyone, everywhere.
Our diverse, remote\-first teams are essential to our success. We empower our Dream Team members with flexibility and resources, fostering an environment where innovation thrives and every contribution is valued and celebrated.
The work you do here will positively impact lives around the world. We stand by our promise: Opportunity Made Possible. In addition to competitive compensation and benefits, we invite you to join us in expanding your skills and helping to reshape the future of work.
At G\-P, we assist organizations in building exceptional global teams in days, not months—streamlining the hiring, onboarding, and management process to unlock growth potential for all.
About The Position
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As a Sr. Principal Program Manager you will be the connective tissue between the functional business areas, their AI transformation and the enterprise tech teams developing automation and agentic solutions. This role is about architecting the shift from traditional roles to tech enabled automation and agentic AI workflows that redefine how our teams work.
This role is responsible for the rigorous management of a multi\-year roadmap that integrates corporate initiatives with a global Finance, HRIS, and Legal transformation.
You will serve as the primary operational link between Finance, HRIS, and Legal business stakeholders. You will ensure that high\-stakes workstreams are delivered on time, within budget, and aligned with corporate ROI targets, while maintaining a focus on a seamless, AI\-augmented operational flow.
What You'll Do
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- Lead the collaboration between the business areas and aligned enterprise tech team to ensure alignment between changing ways of working and tech/AI enabled automation and agentic solutions.
- Collaborate with functional leaders and enterprise tech team on the roadmap and measurement of impact/benefits, adoption and enablement.
- Navigate complex cross\-functional dependencies to keep the engine moving—holding engineering, enterprise applications, and business teams accountable for hitting milestones and delivering measurable ROI on agentic initiatives.
- Portfolio Governance \& Roadmap Orchestration: Establish the standards for project intake, prioritization, and status reporting for the Finance, HRIS, and Legal infrastructure. Manage the "Golden Path" of initiatives that optimize the workflow journey.
- AI Program Lifecycle: Oversee the programmatic rollout of AI\-forward capabilities (e.g., automated legal review, automated financial workflows). Manage the pilot\-to\-production lifecycle and measurable business impact.
- Dependency \& Risk Mitigation: Proactively identify and manage critical path dependencies between Finance, HRIS, and Legal engines and the backend financial/contracting systems. Mediate resource contention across matrixed engineering and design teams.
- Executive Steering \& Communication: Chair high\-level steering committees (SVP/C\-Suite) to provide data\-driven insights into portfolio health, budget variance, and risk\-adjusted delivery timelines.
What we're looking for:
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### Minimum Requirements:
- 15\+ Years in Program Management
- Proven track record of managing $20M\+ portfolios within a global enterprise SaaS environment.
- Matrixed Leadership: Expert experience working across Enterprise Application Engineering and Finance/Legal teams to deliver unified business solutions.
- Methodology Mastery: Advanced proficiency in PMP/PgMP frameworks, focusing on Risk Management, Resource Leveling, and Value Realization.
- Domain Experience: Must\-have experience within HRIS and Legal environments.
- Location: Candidates can be in any US time zone (limited to the US, but open to strong candidates from Latin America).
### Preferred Requirements:
- Experience with Workday ERP, Workday HRIS, and Workday Adaptive.
- Experience with DocuSign.
- Experience with incentive/compensation systems, specifically CaptivateIQ.
The annual gross base salary range for this position is $176,000 \- $221,000 plus variable compensation.
We will consider for employment all qualified applicants, including those with arrest records, conviction records, or other criminal histories, in a manner consistent with the requirements of any applicable state and local laws, including the City of Los Angeles' Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, and the New York City Fair Chance Act.
Actual compensation for this position may vary and will depend on multiple factors such as relevant qualifications, experience, education, and geographic location. For Full\-Time Regular Employees, this position is also eligible for additional compensation as follows:
- Sales Roles: This position is eligible for a commission structure in addition to base salary.
- Non\-Sales Roles: This position is eligible for an annual bonus which is paid dependent on various factors, including and without limitation, individual and company performance in addition to base salary.
Benefits
G\-P values its employees and offers excellent benefits and perks including generous paid parental leave, flexible time off, spending accounts, medical insurance, dental insurance, vision insurance, sabbatical after 5 years and more.
*Individuals residing, or applying to work, in the**United States: California or Philadelphia,Pennsylvania,**please review the following additional information:*
*G\-P will consider qualified applicants with arrest or conviction records in accordance with the California Fair Chance Act, Los Angeles City Fair Chance Act Ordinance, Los Angeles County Fair Chance Act Ordinance, and San Francisco Fair Chance Act Ordinance. Los Angeles applicants can review additional information regarding the Los Angeles City Fair Chance Act here:Fair Chance Initiative for Hiring Ordinance, and Philadelphia applicants can review information pertaining to Philadelphia's Fair Criminal Record Screening Standards Ordinance here:Fair Chance Poster. Any consideration of a candidate's background check with arrest or conviction records will include an individualized assessment based on the factors required by applicable law, including the candidate's specific record and the duties and requirements of the specific job.*
G\-P. Global Made Possible.
*G\-P is a proud Equal Opportunity Employer, and we are committed to building and maintaining a diverse, equitable and inclusive culture that celebrates authenticity. We prohibit discrimination and harassment against employees or applicants on the basis of race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth, and pregnancy\-related conditions), gender identity or expression (including transgender status), sexual orientation, marital status, military service and veteran status, physical or mental disability, genetic information, or any other legally protected status.*
*G\-P also is committed to providing reasonable accommodations to individuals with disabilities. Individuals with disabilities are encouraged to apply for these positions. If you need an accommodation due to a disability during the interview process, please contact us at* *careers@g\-p.com.*
Salary Context
This $176K-$221K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At G-P, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($198K) sits 10% above the category median. Disclosed range: $176K to $221K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
G-P AI Hiring
G-P has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span Remote, US, Boston, MA, US. Compensation range: $221K - $221K.
Location Context
AI roles in Boston pay a median of $215,350 across 442 tracked positions. That's 8% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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