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About This Role
Department Product & Engineering Location Short Hills, NJ Job scope USA Work setting VP Product & Innovation Reporting to Hybrid
About Doceree
Doceree is the world's only AI-powered Operating System for healthcare marketing. We aim to be a catalyst for change, improving healthcare by enabling more meaningful interactions with HCPs. Our patented AI brings deeper context to every HCP touchpoint-understanding who HCPs are, what they're exploring, how they engage, and when they make decisions, all in a privacy-compliant way. With this enriched context, every message becomes clearer, more relevant, and more likely to drive better healthcare outcomes. In our journey of 5 years, we have earned four patents for innovation and even won a Silver award at the Cannes Lions, one of the world's top advertising honors. Over the years, we've also had the opportunity to work with 115+ pharma companies and collaborate with 30+ media publications, 150+ EHR platforms, 2,000+ publisher networks, and 35+ hospitals and health systems. Our ecosystem connects us with more than 6 million verified doctors worldwide. Today, Doceree operates across 25+ countries, with teams based in New Jersey, London, and India.
Technology can connect the fragmented healthcare ecosystem to deliver information when it is most needed to improve patients' outcomes. We are expanding our footprints across the globe and enhancing our services, offering, and developing new products and solutions to address the unmet needs of industry.
Purpose of the Job
Doceree is building the AI-powered Healthcare Marketing Cloud, a next-generation omnichannel platform transforming how pharmaceutical brands engage Healthcare Professionals (HCPs).
Our platform moves beyond vanity metrics like impressions and clicks toward Clinical Intent identifying critical decision-making moments and orchestrating the right engagement, across the right channel, at the right time.
As we continue scaling our AI-native platform, we are looking for an Associate Director/Director of Product Management (AI), to help shape the next evolution of intelligent healthcare marketing infrastructure. This individual will play a key role in defining product strategy, partnering closely with Engineering and AI teams, and translating emerging AI capabilities into enterprise-ready commercial products.
This role will partner closely with Product, Engineering, Sales, and Customer Success teams to ensure our AI platform delivers measurable business impact for pharmaceutical clients.
Key Responsibilities
Product Strategy & Vision
- Lead and influence end-to-end product strategy for the Healthcare Marketing Cloud platform across all modules
- Define and maintain the product roadmap, balancing innovation with commercial priorities
- Conduct competitive analysis and identify positioning opportunities in the omnichannel HCP engagement space
- Present roadmap, progress, and strategic direction to executive leadership and key clients
Product Execution
- Drive the product lifecycle from discovery through launch and iteration with an AI-first approach
- Create prototypes, author high-quality PRDs, user stories, and specifications that enable Engineering to execute with clarity
- Drive weekly release cadences with a lean, fast-moving Engineering and Product team
- Lead products from concept through commercialization in fast-moving, high-growth environments
Cross-Functional Collaboration
- Partner with Data Science to evolve AI-driven orchestration capabilities
- Work with Design to deliver intuitive experiences for campaign configuration, journey building, and analytics
- Partner with Sales and Customer Success teams during strategic client conversations, helping translate platform capabilities into business solutions
- Work closely with leadership on go-to-market alignment, product positioning, and client feedback loops
- Partner with Commercial leadership to align product innovation with go-to-market priorities and evolving client needs
- Collaborate with a high-performing global Product and Engineering organization across the US and India
AI Platform & Architecture
- Partner with Engineering and AI teams on the development of LLM-powered workflows and intelligent orchestration systems
- Contribute to product direction around RAG architectures, model evaluation frameworks, fine-tuning strategies, and emerging agentic AI workflows
- Help define scalable AI infrastructure and product capabilities that support enterprise healthcare marketing use cases
- Work closely with technical teams building MCP server integrations and AI enablement tooling
Qualifications - Experience, Skills & Education
- 5-8+ years of product management experience, including at least 4 years building or scaling AI-native products
- Experience leading products from concept through commercialization in fast-moving, high-growth environments
- Strong understanding of modern AI ecosystems including LLMs, RAG frameworks, model evaluation methodologies, and AI orchestration workflows
- Ability to operate comfortably across technical, commercial, and client-facing conversations
- Strong communication and executive presentation skills
- Proven ability to influence cross-functional stakeholders across Product, Engineering, AI/ML, and Commercial teams
- Comfortable with occasional travel for client meetings, leadership collaboration and global team engagement across the US, UK, and India
Key Competencies
- Experience with healthcare, pharmaceutical, AdTech, MarTech, or other highly-regulated industries
- Exposure to AI infrastructure, agentic systems, or cloud-scale AI products
- Familiarity with MCP servers, fine-tuning strategies, or AI evaluation frameworks
- Experience building enterprise SaaS or platform products with customer-facing impact
- Experience working closely with enterprise clients or supporting AI-driven commercial platforms in customer-facing environments
- You balance strategic thinking with execution - you can define a 2-year vision and also unblock a sprint issue
- You're comfortable with ownership - strategy, specs, design review, launch, iteration - no handoffs to "someone else"
- You have strong opinions, loosely held - you can defend a roadmap decision and change your mind when data says otherwise
- You thrive in ambiguity and move fast despite incomplete information
- You're energized by healthcare impact - the idea that your product might help HCPs access the right clinical information faster motivates you
Why Explore a Career at Doceree
Doceree has been recognized and certified Best places to work NJ 2023 and Great Place to work 2026. If you are passionate about health technology and have a knack for turning complex concepts into compelling narratives, we invite you to apply for this exciting opportunity to contribute to the success of our innovative health tech company.
Doceree USA Benefits
- Competitive Salary Package
- Performance Based Bonuses
- Generous Leave Policy
- Flexible Working Hours
- Health Care Benefits
- 401K + 4% Matching
- Extensive Paid Parental Leave
Doceree DEI Vision & Mission
Doceree DEI (Diversity, Equity & Inclusion) is to create and sustain a one team culture globally with a mission to provide equal opportunity to people from diverse social, cultural, physical and psychological backgrounds and do not discriminate on the basis of race, colour, religion, national origin, sex, age, citizenship status, disability status, genetic information, sexual orientation, or gender identity or expression of an otherwise qualified individual, or membership in any other class protected by applicable law. Doceree fosters an inclusive culture by inculcating a sense of belonging within our ONE team through appropriate behaviours, policies and systems, while also being fair to one and all. To support this Doceree has taken formal strides with a 5C model and 4E's pledge, to ensure, encourage and exhibit our commitment to DEI, and move towards our DEI vision.
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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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Doceree, 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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Doceree AI Hiring
Doceree has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Short Hills, NJ, US, Short Hills, NJ.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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