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About This Role
Bamboo Health is the leader in Real\-Time Care Intelligence™ solutions aimed at improving lives for everyone experiencing physical and behavioral health challenges. We are driven by our mission to empower clients to deliver seamless, high\-quality and cost\-effective care during pivotal moments to improve health outcomes. From coast to coast, Bamboo Health partners with all major retail pharmacy chains, 52 states and territories, 100% of the top 10 best hospitals and more than half of the country’s largest health plans to improve more than 1 billion patient encounters annually. Join us in improving lives during pivotal care moments!
Summary:
Bamboo Health is seeking a Director of Product Management to lead the AI product strategy and roadmap for our Bridge solutions while managing a Product Manager and Product Owner responsible for delivering our AI capabilities. This team owns the AI\-driven patient engagement experience following hospital discharge and other referral patterns—including outreach, intake, automated text messaging, operational and outcomes metrics, provider scheduling, and ongoing optimization of the AI agent experience.
You will own the AI\-driven portion of the patient journey end to end, partnering closely with engineering, clinical, and operations teams, and with the counterpart team that owns our human\-in\-the\-loop platform for the moments where a patient needs a Care Navigator. This is a high\-visibility role at the center of one of our fastest\-growing solutions.
What You’ll Do:
- Own the product strategy and roadmap for the agentic AI experience within our Bridge solutions.
- Drive continuous improvement of AI agent performance, including conversation quality, task completion rates, and escalation accuracy to human\-in\-the\-loop workflows.
- Expand AI capabilities across patient and practice outreach, multilingual support, text messaging, and scheduling.
- Partner with engineering, data science, and clinical stakeholders to translate patient and provider needs into clear, well\-scoped product requirements.
- Define and track success metrics for the automated portions of the patient journey, using data to prioritize the roadmap.
- Collaborate with the BIH human\-in\-the\-loop product team to ensure a seamless handoff between AI\-led and human\-led workflows.
- Lead, coach, and develop a Product Manager and Product Owner, fostering strong product management practices and setting a high bar for prioritization, roadmap execution, PRD quality, and user story/epic definition.
- Partner with sales, implementation, and client success teams to support new client rollouts and ongoing deployments.
What Success Looks Like…
In 3 months…
- Develop deep fluency in the Bridge AI product — the agent experience, current roadmap, key metrics, and how patients and practices move through the journey.
- Build strong working relationships across the AI team, the BIH human\-in\-the\-loop team, engineering, clinical, and customer\-facing teams.
- Get up to speed on active client implementations and identify any near\-term risks or gaps in the current roadmap.
In 6 months…
- Establish an organized, prioritized product roadmap for the AI team, with clear PRDs, user stories, and epics.
- Put in place consistent product processes — prioritization framework, roadmap cadence, and requirements documentation — across the team.
- Demonstrate measurable improvement in at least one AI agent performance metric (e.g., task completion rate, escalation accuracy).
In 12 months…
- Own a mature, scaling product area with measurable business outcomes across a growing client base.
- Expand AI capabilities into new areas of the patient journey.
- Be recognized as a go\-to product leader for Bridge's AI capabilities, internally and with clients.
What You Need:
- 10\+ years of experience in product management, including 5\+ in healthcare, with a focus on strategic product development and innovation.
- Track record of owning a product area end to end — strategy, roadmap, execution, and measurable outcomes.
- Strong ability to translate ambiguous problems into clear prioritization, PRDs, and user stories.
- Experience leading and developing product teams, including direct management of Product Managers and/or Product Owners.
- Excellent cross\-functional collaboration skills, including with engineering, clinical, and go\-to\-market teams.
- Experience participating in client facing / sales meetings and presentations.
- Experience with agentic AI, conversational AI, or LLM\-based product experiences.
- Experience with care coordination, care navigation, or post\-discharge patient engagement.
- Experience scaling a product through multiple health system or payer client implementations.
- Comfort using or learning AI\-supported tools (e.g., ChatGPT, CoPilot, or role\-specific tools) to improve daily workflows.
- A forward\-thinking, curious mindset with an openness to experimenting with new technologies.
- Strong analytical and problem\-solving skills, with sound judgment and creativity in designing solutions.
- Proven ability to thrive in fast\-paced, high\-growth, and rapidly evolving environments.
- Ability to work effectively in a remote\-first environment, ensuring high\-quality virtual interactions with minimal distractions.
- The ability to travel periodically or frequently for work.
What You Get:
- Join one of the most innovative healthcare technology companies in the country.
- Have the autonomy to build something with an enthusiastically supportive team.
- Learn from working at the highest levels and on the most strategic priorities of the company, including from world class investors and advisors.
- Receive competitive compensation including health, dental, vision and other benefits.
Belonging at Bamboo
We Care. \#BambooHealthValuesCare
Every human being has the right to the best possible healthcare. Our Real\-Time Care Intelligence™ solutions enable healthcare professionals to see and treat every individual as a whole person by providing the right information, at the right time – regardless of physical, behavioral or social barriers.
We’re a great place to work because we care. We continually seek to learn about our differences and ensure the unique perspectives and contributions of all employees are welcome, valued and celebrated.
Our commitment to making a positive impact starts by recognizing and leveraging our differences, building inclusive teams and cultivating a sense of belonging.
*Bamboo Health is proud to provide equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.*
*This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.*
Bamboo Health GDPR/RODO
To protect our applicants from fraudulent recruitment activity, we recommend that all applicants verify the validity of an interview and hiring process by visiting our website www.bamboohealth.com. All valid job postings will be listed on our careers page. Bamboo Health does not conduct interviews via text and will not request sensitive information such as banking details during the application process.
*\#LI\-Remote*
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 Bamboo Health, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
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.
Bamboo Health AI Hiring
Bamboo Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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