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About This Role
We’re hiring at Pitney Bowes, where top talent builds meaningful careers and lasting impact. We Move fast, Deliver excellence, and Win together…that’s The Pitney Bowes way. Here, how we work matters just as much as what we achieve.
We’re looking for people who:
- Act with urgency, accountability, and purpose
- Deliver high quality work with consistency and pride
- Collaborate effectively and elevate those around them
- Focus on outcomes that drive impact and growth
Job Description:
You Are
A technical product and delivery leader who thrives at the intersection of AI, engineering, and business outcomes. You enjoy bringing structure to ambiguity, translating strategy into execution, and helping teams deliver impactful solutions. You are comfortable working with engineers, architects, product leaders, and security teams to ensure AI capabilities are delivered responsibly, efficiently, and at scale.
You Will
- Own and manage the backlog and delivery cadence for one or more AI product teams, ensuring priorities align with business objectives and quarterly goals.
- Partner with engineering, product, architecture, security, and business stakeholders to define outcomes, priorities, and delivery plans.
- Translate strategic objectives and evolving business needs into epics, user stories, acceptance criteria, and actionable work for development teams.
- Define success criteria for AI-powered capabilities, including evaluation methods, governance requirements, performance expectations, observability, and operational readiness.
- Prioritize work across multiple stakeholders while balancing business value, technical considerations, and delivery commitments.
- Maintain visibility into platform capabilities, integrations, tools, and governance requirements.
- Facilitate Agile ceremonies including sprint planning, standups, reviews, and retrospectives, ensuring they remain effective and focused.
- Monitor delivery health through key metrics such as velocity, cycle time, backlog readiness, and dependency management.
- Proactively identify and remove obstacles that impact delivery, coordinating across teams to keep work progressing smoothly.
- Support estimation, roadmap planning, and scope discussions in an environment that balances innovation with execution.
- Coordinate cross-functional initiatives to ensure integrations, dependencies, and shared deliverables are planned and managed effectively.
- Partner with architects and engineering leaders to translate technical decisions into deliverable outcomes.
- Maintain traceability between business requirements, technical implementation, and delivered AI functionality.
- Support AI governance activities, including model reviews, data handling considerations, human-in-the-loop workflows, audit readiness, and change management.
- Monitor platform performance, model utilization, and cost considerations to support informed decision-making.
- Lead proof-of-concept efforts as structured experiments with clear objectives, measurable outcomes, and documented recommendations.
- Serve as a trusted source of delivery status, priorities, and progress across product, engineering, and security stakeholders.
You Bring
- 5+ years of experience in technical product ownership, technical program management, engineering delivery, or a similar software leadership role.
- Experience owning and prioritizing a product backlog in an Agile environment.
- Experience delivering AI/ML or LLM-powered capabilities into production environments.
- Strong understanding of modern AI application patterns, including prompting, RAG, tool/function calling, agent orchestration, evaluation frameworks, and guardrails.
- Technical fluency with APIs, integrations, authentication models, and software architecture concepts.
- Experience facilitating Scrum or Kanban practices across distributed teams.
- Strong written and verbal communication skills, with the ability to engage both technical and executive audiences.
- The ability to balance competing priorities and translate complex business needs into clear, actionable requirements.
- Comfort operating in fast-paced environments where experimentation, learning, and adaptation are part of the process.
Preferred Qualifications
- Experience with Amazon Bedrock/AgentCore, Azure AI Foundry, Vertex AI Agent Builder, or similar AI platforms.
- Experience designing evaluation frameworks for LLM-based systems.
- Familiarity with enterprise data platforms, data governance, and AI governance practices.
- Experience working in regulated or enterprise-scale environments.
- Background in logistics, shipping, supply chain, or B2B SaaS.
- Agile certifications such as CSPO, PSPO, CSM, PSM, or SAFe.
Compensation: The wage range for this position is $172,000-$192,000 per year. Actual compensation will be based on skills and experience as they relate to the job requirements and the location where you will be performing the job.
Location: This is a hybrid role, with 4 days in the Shelton, CT office required. This hybrid status may change based on business need.
Sponsorship:
Must be legally authorized to work in the US. Employer will not sponsor position for employment visa status now or in the future (ex. H-1B).
We will:
- Provide the opportunity to grow and develop your career
- Offer an inclusive environment that encourages diverse perspectives and ideas
- Deliver challenging and unique opportunities to contribute to the success of a transforming organization
- Offer comprehensive benefits globally(PB Benefits and Wellbeing Programs)
Pitney Bowes is an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard for race, color, sex, religion, national origin, age, disability (mental or physical), veteran status, sexual orientation, gender identity, or any other consideration made unlawful by applicable federal, state, or local laws.
All qualified applicants, including Veterans and Individuals with Disabilities, are encouraged to apply.
All interested individuals must apply online. Individuals with disabilities who cannot apply via our online application should refer to the alternate application options via our Individuals with Disabilities link.
Salary Context
This $172K-$192K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Pitney Bowes, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($182K) sits 14% below the category median. Disclosed range: $172K to $192K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Pitney Bowes AI Hiring
Pitney Bowes has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Shelton, CT, US. Compensation range: $192K - $192K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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