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About This Role
About the Staff Machine Learning Engineer at Headspace:
*The AI \& Machine Learning group at Headspace is a dynamic and innovative group whose mission is to improve the experiences of our members and clinicians through the mindful application of AI. Our group builds conversational AI systems including Ebb, as well as search, recommendation \& personalization systems that power the Headspace experience. The Staff ML Engineer for Personalization will be part of the team developing and delivering the holistic system to enable the personalization of the Headspace experience, including content recommendation, personalized nudges \& notifications, and a tailored conversational AI experience with Ebb. You'll have the opportunity to lead the vision, alignment, development, deployment, and adoption of these solutions, helping to realize Headspace's mission to improve the health and happiness of the world.*
What you will do:
- Technical Leadership: Lead the development of recommender systems for Headspace meditation \& mindfulness content as well as other backend services that enable a personalized member experience including search, a user knowledge graph, and conversational AI memory. This will involve developing and deploying complex, scalable AI models and applications. Drive impactful ML technology initiatives that will shape the delivery of and access to mental healthcare. Serve as a go\-to expert and mentor, exemplifying excellence in AI/ML engineering and inspiring others to pursue technical career growth.
- Shape Personalization Architecture: Contribute to the design, development, and evolution of our AI systems, taking it from high\-level vision to robust implementation, enabling production\-ready AI capabilities.
- Collaborative Problem\-Solving: Partner with our team of software engineers,ML engineers, and MLOps engineers, and our Product \& Clinical leads to build high quality features that improve members' lives.
What you will bring:
Required Skills:
- Bachelor of Science degree or higher in Computer Science, Statistics, Mathematics or a related field OR equivalent experience
- 5\+ years of ML engineering experience in an academic or professional setting, programming in Python
- 5\+ years of experience with any of the following fundamental technologies: vector search, embedding models, recommender systems, supervised, unsupervised machine learning, deep learning, reinforcement learning, LLM orchestration, RAG systems.
- 3\+ years of experience with modern NLP tools and machine learning libraries (scikit\-learn, PyTorch, TensorFlow, spaCy)
- Experience with unit, integration, and end\-to\-end testing, version control
- Strong problem solving and communication skills and ability to influence across internal organizations
- Mentorship of junior engineers and contribution to DEIB initiatives
Preferred Skills:
- Master's degree in relevant field or equivalent experience
- Professional experience with clinical and/or healthcare applications of machine learning
- Familiarity with current ML literature
- Experience with implementation of robust and highly scalable services
- Experience with AWS, including SageMaker, Lambda, S3, DynamoDB, IAM
Location: This role is open to candidates across the US, with preferred locations in San Francisco, CA (hybrid), New York City, NY (remote), and Seattle, WA (remote). Candidates must permanently reside in the US full\-time.
*If your primary residence is in the greater San Francisco Bay Area, this role follows our hybrid model, with 3 days per week in office to support in\-person collaboration. Your recruiter will share more details.*
Pay \& Benefits:
The anticipated new hire base salary range for this full\-time position is $140,400\-$195,000 \+ equity \+ benefits.
*For candidates based in San Francisco, New York City, or Seattle, a separate salary range of $179,400\-$224,250 applies, consistent with our location\-based compensation philosophy.*
Our salary ranges are based on the job, level, and location, and reflect the lowest to highest geographic markets where we are hiring for this role within the United States. Within this range, individual compensation is determined by a candidate's location as well as a range of factors including but not limited to: unique relevant experience, job\-related skills, and education or training.
*Your recruiter will provide more details on the specific salary range for your location during the hiring process.*
At Headspace, base salary is but one component of our Total Rewards package. We're proud of our robust package inclusive of: base salary, stock awards, comprehensive healthcare coverage, monthly wellness stipend, retirement savings match, lifetime Headspace membership, generous parental leave, and more. Additional details about our Total Rewards package will be provided during the recruitment process.
About Headspace
Headspace exists to provide every person access to lifelong mental health support. We combine evidence\-based content, clinical care, and innovative technology to help millions of members around the world get support that's effective, personalized, and truly accessible whenever and wherever they need it.
At Headspace, our values aren't just what we believe, they're how we work, grow, and make an impact together. We live them daily: Make the Mission Matter, Iterate to Great, Own the Outcome, and Connect with Courage. These values shape our decisions, guide our collaborations, and define our culture. They're our shared commitment to building a more connected, human\-centered team—one that's redefining how mental health care supports people today and for generations to come.
Why You'll Love Working Here:
A mission that matters—with impact you can see and feel
A culture that's collaborative, inclusive, and grounded in our values
The chance to shape what mental health care looks like next
Competitive pay and benefits that support your whole self
How we feel about Diversity, Equity, Inclusion and Belonging:
Headspace is committed to bringing together humans from different backgrounds and perspectives, providing employees with a safe and welcoming work environment free of discrimination and harassment. We strive to create a diverse \& inclusive environment where everyone can thrive, feel a sense of belonging, and do impactful work together.
As an equal opportunity employer, we prohibit any unlawful discrimination against a job applicant on the basis of their race, color, religion, gender, gender identity, gender expression, sexual orientation, national origin, family or parental status, disability\*, age, veteran status, or any other status protected by the laws or regulations in the locations where we operate. We respect the laws enforced by the EEOC and are dedicated to going above and beyond in fostering diversity across our workplace.
- *Applicants with disabilities may be entitled to reasonable accommodation under the terms of the Americans with Disabilities Act and certain state or local laws. A reasonable accommodation is a change in the way things are normally done which will ensure an equal employment opportunity without imposing undue hardship on Headspace.* *Please inform our Talent team by filling out* *this form* *if you need any assistance completing any forms or to otherwise participate in the application or interview process.*
*Headspace participates in the* *E\-Verify Program**.*
*Privacy Statement*
*All member records are protected according to ourPrivacy Policy. Further, while employees of Headspace (formerly Ginger) cannot access Headspace products/services, they will be offered benefits according to the company's benefit plan. To ensure we are adhering to best practice and ethical guidelines in the field of mental health, we take care to avoid dual relationships. A dual relationship occurs when a mental health care provider has a second, significantly different relationship with their client in addition to the traditional client\-therapist relationship—including, for example, a managerial relationship.*
*As such, Headspace requests that individuals who have received coaching or clinical services at Headspace wait until their care with Headspace is complete before applying for a position. If someone with a Headspace account is hired for a position, please note their account will be deactivated and they will not be able to use Headspace services for the duration of their employment.*
*Further, if Headspace cannot find a role that fails to resolve an ethical issue associated with a dual relationship, Headspace may need to take steps to ensure ethical obligations are being adhered to, including a delayed start date or a potential leave of absence. Such steps would be taken to protect both the former member, as well as any relevant individuals from their care team, from impairment, risk of exploitation, or harm.*
For how how we will use the personal information you provide as part of the application process, please see: https://www.headspace.com/applicant\-notice
Salary Context
This $140K-$224K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Headspace, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $140K to $224K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Headspace AI Hiring
Headspace has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $224K - $224K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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