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About This Role
The Senior Manager, Print Production \& Mailing is responsible for leading the strategy, execution, and continuous improvement of all print and mail operations. This role oversees end\-to\-end production workflows, vendor management, regulatory compliance, quality assurance, budgeting, and operational performance. The ideal candidate combines deep technical expertise in print and mail production with strong leadership, operational rigor, and a continuous improvement mindset. This role partners cross\-functionally with Marketing, Communications, Compliance, Operations, IT, and external vendors to ensure timely, cost\-effective, and compliant delivery of printed and mailed materials.Key Responsibilities
- Strategic Leadership
+ Develop and execute the print and mail production strategy aligned with organizational goals.
+ Drive operational excellence, process optimization, and cost containment initiatives.
+ Identify and implement automation and technology enhancements to improve scalability and accuracy.
+ Establish KPIs, dashboards, and reporting frameworks to monitor performance and service levels.
- Production \& Operations Management
+ Oversee end\-to\-end print production lifecycle, including file intake, proofing, prepress, printing, finishing, inserting, and distribution.
+ Ensure accurate and timely execution of high\-volume, time\-sensitive mailings.
+ Manage internal teams and/or third\-party print and mail vendors.
+ Maintain detailed production schedules and ensure adherence to SLAs.
+ Oversee inventory management for print materials and mailing supplies.
- Vendor \& Budget Management
+ Lead vendor selection, contract negotiation, and performance management.
+ Conduct regular vendor performance reviews and audits.
+ Manage departmental budget, forecasting, and cost analysis.
+ Drive competitive bidding processes to optimize cost and quality.
- Compliance \& Quality Assurance
+ Ensure compliance with postal regulations, data privacy laws, and industry\-specific regulatory requirements.
+ Maintain strict quality control standards to minimize errors and rework.
+ Oversee data security protocols related to printed member/customer communications.
+ Partner with Compliance and Legal teams to validate regulated communications.
- Risk Management \& Controls
+ Establish and maintain internal controls for file handling, version control, and mailing accuracy.
+ Develop contingency plans to mitigate production disruptions.
+ Lead root cause analysis for production or mailing errors and implement corrective actions.
- Cross\-Functional Collaboration
+ Partner with Marketing and Communications on campaign planning and execution.
+ Collaborate with IT on data integration, automation, and system enhancements.
+ Work with Operations and Customer Service to ensure communications support business needs.
+ Provide production guidance during creative development to ensure feasibility and cost efficiency.
- Team Leadership \& Development
+ Lead, mentor, and develop a high\-performing production team.
+ Define clear roles, responsibilities, and performance expectations.
+ Foster a culture of accountability, collaboration, and continuous improvement.
+ Support succession planning and professional development.
Minimum Qualifications
- 8\+ years of experience in print production and mailing operations.
- Experience managing high\-volume, time\-sensitive, and regulated communications.
- Experience with USPS regulations and postal optimization strategies (e.g., presort, commingling).
- Knowledge of print production workflows (offset, digital, variable data printing).
- Familiarity with mail processing, inserting equipment, and postal software.
- Experience with production management systems and workflow automation tools.
Preferred Qualifications
- Lean, Six Sigma, or PMP certification preferred.
- 3–5\+ years in a leadership or management role.
- Strong background in vendor management and contract negotiation.
- Proficiency in Microsoft Office and reporting tools.
- Understanding of data security and file transfer protocols.
Working Conditions
- Hybrid or on\-site depending on operational needs.
- May require periodic vendor site visits.
- Occasional extended hours during peak production cycles.
Hiring Range\*:
- Greater New York City Area (NY, NJ, CT residents): $122,900 \- $188,020
- All Other Locations (within approved locations): $105,100 \- $160,480
As a candidate for this position, your salary and related elements of compensation will be contingent upon your work experience, education, licenses and certifications, and any other factors Healthfirst deems pertinent to the hiring decision.
In addition to your salary, Healthfirst offers employees a full range of benefits such as, medical, dental and vision coverage, incentive and recognition programs, life insurance, and 401k contributions (all benefits are subject to eligibility requirements). Healthfirst believes in providing a competitive compensation and benefits package wherever its employees work and live.
- *The hiring range is defined as the lowest and highest salaries that Healthfirst in “good faith” would pay to a new hire, or for a job promotion, or transfer into this role.*
105100\-160480
Salary Context
This $105K-$188K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Healthfirst (New York), 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($146K) sits 12% below the category median. Disclosed range: $105K to $188K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Healthfirst (New York) AI Hiring
Healthfirst (New York) has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, NY, US. Compensation range: $99K - $188K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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