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Apply Now →About This Role
Honor Technology’s mission is to change the way society cares for older adults. As a leader in aging care innovation, Honor provides the technology, tools, and services that empower older adults to live life on their own terms. Honor’s growing portfolio includes its consumer care brand, Home Instead, Inc., the world’s leading provider of in\-home care for older adults. With a global franchise network and more than 100,000 Care Pros, Home Instead delivers over 50 million hours of personalized care annually.
Together, Honor and Home Instead are setting a new standard for aging in place, backed by powerful technology, compassionate care, and a commitment to aging on your own terms.
Join us to create a new and better aging experience for our clients, their families, and our Care Professionals.
About Honor
Our mission is simple: change the way we care for our parents so they can age safely at home.
How we accomplish that mission is anything but simple. Delivering reliable home care requires coordinating people, schedules, geography, skills, preferences, and changing needs across a complex operating environment.
Honor combines technology, operations, and the local expertise of our partner agencies to improve how care is delivered at scale. Our platform supports the caregivers who provide care, the teams who coordinate it, and the families who depend on it.
We are looking for a Senior Manager, Data Science to lead a team applying advanced data science and AI to some of Honor’s most important operational challenges.
About the role
You will lead the development of models and decision systems that improve how Honor matches caregivers and clients, constructs sustainable schedules, allocates capacity, prioritizes operational work, and responds to changing conditions.
This is not an analytics support role. Your team will work directly with Product, Engineering, and Care Operations to turn difficult operating problems into production systems that improve measurable outcomes.
You will contribute to Honor’s broader Data Science and Applied AI direction, help shape the team’s roadmap, and remain hands\-on as a player\-coach.
What you’ll do
- Help shape the Data Science and Applied AI roadmap, identifying where predictive modeling, optimization, experimentation, forecasting, causal inference, and modern AI can create the greatest impact.
- Lead the development of production models and decision\-support systems that improve matching, scheduling, operational prioritization, and service reliability.
- Apply modern AI, including agentic workflows, to improve how teams analyze information, make decisions, and execute operational work.
- Partner with Product, Engineering, and Operations from problem definition through deployment, adoption, and iteration.
- Establish strong standards for evaluating, monitoring, and responsibly deploying machine learning and AI\-enabled systems.
- Lead, coach, and grow the team while remaining hands\-on in high\-priority projects as a player\-coach.
Examples of problems you may solve
- Improving caregiver\-client matching while balancing fit, continuity, availability, and travel.
- Building more reliable and sustainable schedules for caregivers and clients.
- Using data science and AI to identify operational risks earlier and help teams focus where they can have the greatest impact.
About you
- 7\+ years of experience in data science, machine learning, operations research, applied AI, or a related field, including 3\+ years leading teams.
- Have built or led the development of models or decision systems that operated in production and influenced meaningful business or customer outcomes.
- Can determine whether a problem calls for prediction, optimization, experimentation, simulation, workflow redesign, an AI agent, or no model at all.
- Understand that successful applied AI requires more than choosing a model. It requires clear objectives, reliable data and tools, rigorous evaluation, thoughtful system design, and appropriate human oversight.
- Experience partnering with Product, Engineering, Operations, and senior business stakeholders on ambiguous, cross\-functional problems.
- Comfortable working with imperfect operational data while maintaining a high standard for measurement and technical rigor.
- Can communicate clearly with technical, operational, and executive audiences.
- Have a bias toward action and an iterative approach to problem\-solving. You are willing to inspect the data, understand the workflow, prototype solutions, and revise your view.
- Are an effective people leader who can set clear expectations, develop talent, prioritize work, and build a strong technical culture.
Helpful experience
Experience with marketplace, logistics, workforce, scheduling, healthcare, or other complex operating systems is especially valuable, as is experience with optimization, forecasting, recommender systems, causal inference, agentic systems, or AI evaluation.
A bachelor’s, master’s, or doctoral degree in a quantitative or technical field is welcome, but we care most about demonstrated ability, judgment, leadership, and impact.
Why this role is different
Many companies have interesting datasets. Honor has a dynamic operating environment in which technical decisions directly affect caregivers, care teams, families, and older adults. The opportunity is to build systems that do more than predict outcomes. They will help Honor decide what to do next, execute more effectively, and continuously learn from the results.
*Our range reflects the hiring range for this position. We use national average to determine pay as we are a remote first company. Individual pay is based on a number of factors including qualifications, skills, experience, education, and training.*
*Base pay is just a part of our total rewards program. Honor offers generous equity packages that increase with position level and responsibilities, and a 401K with up to a 4% employer match.*
*We provide medical, dental and vision coverage including zero cost plans for employees. Short Term Disability, Long Term Disability and Life Insurance are fully employer paid with a voluntary additional Life Insurance option. We offer a generous time off program, mental health benefits, wellness program, and discount program.*
Hiring Salary Range
$228,600—$254,000 USD
At Honor, we put people first. Our leadership culture is guided by Leadership Principles that prioritize integrity, compassion, and excellence. We offer a unique opportunity to lead with purpose and make a meaningful impact no matter your role.
Honor is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy), national origin, age, disability, genetic information, political affiliation or belief.
Salary Context
This $228K-$254K range is above the 75th percentile 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 Honor, 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 ($241K) sits 10% above the category median. Disclosed range: $228K to $254K.
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.
Honor AI Hiring
Honor has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $254K - $254K.
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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