Interested in this AI/ML Engineer role at Pegasystems?
Apply Now →About This Role
Meet Our Team:
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Join an agile, dynamic legal team dedicated to privacy compliance and closing deals efficiently. Our team thrives on collaboration and leadership, building strong, trusting relationships across the company to achieve business objectives. We value a strategic and proactive mindset, commitment to professionalism and client\-centric approach.
Picture Yourself at Pega:
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As a Senior Privacy, AI and Cybersecurity Counsel, you will be a key member of Pega’s global legal team supporting the company in a range of matters including commercial negotiations related to Pega Cloud, privacy, AI and security matters, and partnering with other teams across the company to bring AI, privacy and cybersecurity advice.
What You'll Do at Pega:
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As a senior subject matter expert in privacy, AI and cybersecurity:
- Collaborate with the teams negotiating commercial contracts to assess risks in contractual terms and to negotiate complex client contracts concerning Security, Privacy, and AI in Pega’s Subscription Services.
- Partner with product teams to support Pega’s business objectives.
- Demonstrate independent situational business judgment and strong business acumen.
- Provide compliance advice aligned with the company’s global business goals, addressing international privacy, AI, and security laws and regulations.
- Guide the data owners on AI reviews, Privacy and Security by Design, Data Mapping, Privacy Impact Assessments, Marketing reviews, and Security inquiries.
Who You Are:
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- A self\-starter, business\-friendly, agile negotiator with a growth mindset, and AI/privacy/security lawyer with excellent communication skills.
- You are a fully qualified lawyer with extensive knowledge and a minimum of 7 years’ experience in sophisticated technology companies advising on AI, cybersecurity, and privacy law.
- Demonstrate a history of simultaneously reducing risk and accelerating the contracting process.
- An expert at building strong, trusting relationships with sales teams and clients.
- Capable of explaining complex privacy, AI, and security concepts to a nonspecialist audience.
- Embrace Pega’s Values of being Innovative, Inclusive, Passionate, Engaging, Genuine, and Adaptable.
What You've Accomplished:
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- 7\+ years of experience in delivering expert privacy, AI and cybersecurity advice within a dynamic SAAS/IASS technology company.
- Proven track record of 7\+ years in successfully negotiating privacy and security issues in SAAS or IASS agreements.
- IAPP certifications\- CIPP/US, CIPP/E, CIPM, CIPT (preference for Privacy Law Specialist or Fellow in Information Privacy Certification).
- IAPP certification in Artificial Intelligence.
- ISACA Cloud or Security certifications (CISSP preferred) will be advantageous.
- Extensive expertise in crafting and refining template and backup legal language.
Pega Offers You:
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- Gartner Analyst acclaimed technology leadership across our categories of products.
- Continuous learning and development opportunities.
- An innovative, inclusive, agile, flexible, and fun work environment.
- Competitive global benefits program inclusive of pay \+ bonus incentive, employee equity in the company.
Additional Information
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Base salary range for this role is 149,500 \- 227,800 USD annually. This role may also be eligible for annual bonus OR commission, as well as benefits and other incentives.
The final compensation will be determined during the offer process based on the candidate's education, experience, skills, and qualifications, as well as market conditions and may vary from the posted range. We will share an information on benefits, bonus/commission, and other pay components for this role at the relevant recruitment stage.
\#LI\-JP1
AI in Action – Responsible Use of AI in Recruitment
Pega embraces the responsible use of artificial intelligence (AI) to improve efficiency, consistency, and fairness across our business. We encourage thoughtful and ethical adoption of AI technologies that support people—not replace them. We may use AI‑enabled tools in our recruitment process. These tools are designed to assist us by providing insights and operational support.
All hiring decisions are made based on human review and judgment. You may have the right to request human review, provide additional information, or raise questions about how such tools are used.
Culture
At Pegasystems, we foster an environment where people feel valued and empowered to contribute their best. With global clients across industries and regions, we know our success depends on the unique perspectives, experiences, and talents of our people. Ours is a workplace where everyone can grow, collaborate, and deliver meaningful outcomes.
We encourage candidates from all backgrounds and experiences and focus on the core competencies and mindset needed to thrive in a role.
As an Equal Opportunity employer, Pegasystems will not discriminate in its employment practices due to an applicant's race, color, religion, sex, sexual orientation, gender identity, national origin, age, genetic information, veteran or disability status, or any other category protected by law.
Export Compliance
For positions requiring access to technical data subject to export control regulations such as this, Pegasystems may need to obtain export license approval from the U.S. Government and EU Authorities for certain individuals.
Accommodations
If you require reasonable accommodations under the Americans with Disabilities Act (US only) or comparable regional regulations in completing this application, interviewing, completing any pre\-employment testing, or otherwise participating in the employee selection process, please contact us here or contact (US only) 1\-888\-PEGA\-NOW and/or 225 Wyman Street Waltham, MA 02451 ATTN: Benefits.
Salary Context
This $149K-$227K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Pegasystems, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($188K) sits 14% below the category median. Disclosed range: $149K to $227K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Pegasystems AI Hiring
Pegasystems has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in MA, US. Compensation range: $227K - $227K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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