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About This Role
Our Mission
As the world’s number 1 job site\*, our mission is to help people get jobs. We strive to cultivate an inclusive and accessible workplace where all people feel comfortable being themselves. We're looking to grow our teams with more people who share our enthusiasm for innovation and creating the best experience for job seekers.
(\*Comscore, Total Visits, March 2025\)
Day to Day
As a Machine Learning Engineering Fellow at Indeed, your technical leadership will play an integral role in accelerating improvements to our core search and recommendations platforms. You will have the opportunity to collaborate with smart, caring, and inclusive co\-workers, including Product Managers, Software Engineers, User Experience, and, of course, your fellow MLEs. You'll be working across our retrieval, ranking, metadata, resume, and machine learning platform teams. If you love learning about a variety of different machine learning systems, identifying areas for improvement, and managing across teams to deliver on your vision, then this is a great role for you.
The Opportunity \& The Power of Indeed Matching: With tens of millions of daily users and billions of structural data points, Indeed possesses one of the most powerful and scaled multi\-sided matching ecosystems in the world. This role represents a unique, step\-function opportunity to move beyond incremental optimization and architect the next generation of AI\-first matching. You will directly spearhead transformative innovations in advanced generative retrieval, extreme multi\-label classification clustering, and deep semantic ranking—fundamentally redefining how human potential connects with global employment opportunities at a massive scale.
You’ll observe the impact of your work every day. So if you’re a machine learning engineer who is passionate about driving innovation that helps people get jobs, we want you to apply!
You will be able to work from anywhere within the United States for this role.
Responsibilities
Identify, architect, and drive innovative machine learning solutions across the Indeed platform spanning multiple teams and organizations
Collaborate with cross\-functional partners to clarify requirements and plan large\-scale initiatives
Partner with senior leadership to develop and influence long\-term technical strategy
Guide and influence people at all levels of the company in delivering significant initiatives
Mentor and support the growth of machine learning engineers and emerging engineering leaders
Propose and oversee initiatives that improve the efficiency and productivity of the machine learning organization
Contribute hands\-on when needed by diving into code and sharing machine learning engineering best practices across the organization
Skills/Competencies
Requires a Bachelor’s degree in Computer Science, Mathematics, Statistics, or related field and a minimum of 15 years of related experience; or a Master’s degree with a minimum of 12 years of experience; or a PhD with 8 years experience
Experience guiding through influence and delivering predictable results in complex organizations
Proven ability to translate product direction into machine learning architecture and strategy
Effective and inclusive verbal and written communication to a variety of different audiences
Experience coding in Java, C, C\+\+, or Go in backend development; Tensorflow, PyTorch, Sagemaker, or equivalent ML Platforms for model development; and Python or R for data analytics
Experience with SQL Databases such as Presto, AWS Athena, Postgres, BiqQuery, or equivalent
Salary Range Transparency
Tier 1 \- United States of America $227,000 \- 341,000 USD per year
Tier 2 \- United States of America $253,000 \- 379,000 USD per year
Tier 3 \- United States of America $278,000 \- 416,000 USD per year
Tier 4 \- United States of America N/A
Tier 5 \- United States of America $316,000 \- 474,000 USD per year
Salary Range Disclaimer
The salary range for this role reflects the minimum and maximum compensation for the role. Offers are typically made between the range minimum and the range midpoint. Actual compensation will be determined based on job\-related skills, experience, and expertise, as evaluated during the interview process. The range(s) listed is just one component of Indeed's total compensation package for employees. Other rewards may include quarterly bonuses, Restricted Stock Units (RSUs), a Paid Time Off policy, and many region\-specific benefits. Compensation may also vary based on where a role is performed, as work locations are grouped into geographic pay tiers to reflect cost of labor differences in different geographic markets. Candidates can view geographic pay tiers by location on our career site (https://www.indeed.com/careers/paytiers), and recruiters can confirm how location is considered for a specific role.
Benefits \- Health, Work/Life Harmony, \& Wellbeing
We care about what you care about. We have a multitude of benefits to support Indeedians, as well as their pets, kids, and partners including medical, dental, vision, disability and life insurance. Indeedians are able to enroll in our company’s 401k plan, as well as an equity\-based incentive program. Indeedians will also receive open paid time off, 11 paid holidays a year and up to 26 weeks of paid parental leave. For more information, select your country and learn more about our employee benefits, program, \& perks at https://www.indeed.com/careers/benefits!
Equal Opportunities and Accommodations Statement
Indeed is deeply committed to building a workplace and global community where inclusion is not only valued, but prioritized. We’re proud to be an equal opportunity employer, seeking to create a welcoming and diverse environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, family status, marital status, sexual orientation, national origin, genetics, neuro\-diversity, disability, age, or veteran status, or any other non\-merit based or legally protected grounds.
Indeed provides reasonable accommodations to qualified individuals with disabilities in the employment application process. To request an accommodation, please visit https://www.indeed.com/careers/accommodations. If you are requesting accommodation for an interview, please reach out at least one week in advance of your interview.
For more information about our commitment to equal opportunity/affirmative action, please visit our Careers page (https://www.indeed.com/careers).
Inclusion \& Belonging
Inclusion and belonging are fundamental to our hiring practices and company culture, forming an integral part of our vision for a better world of work. At Indeed, we’re committed to the wellbeing of our employees and on a mission to make this the best place to work and thrive. We believe that fostering an inclusive environment where every employee feels respected and accepted benefits everyone, fueling innovation and creativity.
We value diverse experiences, including those who have had prior contact with the criminal legal system. We are committed to providing individuals with criminal records, including formerly incarcerated individuals, a fair chance at employment.
Those with military experience are encouraged to apply. Equivalent expertise demonstrated through a combination of work experience, training, military experience, or education is welcome.
Indeed’s Employee Recruiting Privacy Policy
Like other employers Indeed uses our own technologies to help us find and attract top talent from around the world. In addition to our site’s user and privacy policy found at https://www.indeed.com/legal, we also want to make you aware of our recruitment specific privacy policy found at https://www.indeed.com/legal/indeed\-jobs.
Agency Disclaimer
Indeed does not pay placement fees for unsolicited resumes or referrals from non\-candidates, including search firms, staffing agencies, professional recruiters, fee\-based referral services, and recruiting agencies (each individually, an "Agency"), subject to local laws. An Agency seeking a placement fee must obtain advance written approval from Indeed's internal Talent Acquisition team and execute a fee agreement with Indeed for each job opening before making a referral or submitting a resume for that opening.
AI Notice
Indeed is committed to ensuring fairness and transparency throughout our hiring process. We use artificial intelligence (AI) tools to assist in the screening, assessment, and selection of applicants for this position by analyzing information provided in resumes and applications. Our use of AI does not replace human decision\-making.
Unless otherwise notified, Indeed does not use AI constituting an AEDT or an ADMT as those tools are defined in applicable laws.
The deadline to apply to this position is 7/15/26\. Job postings may be extended at the hiring team’s discretion based on applicant volume.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Reference ID: 47326
Reference ID: 47326
Pay: $227,000\.00 \- $474,000\.00 per year
Work Location: Remote
Salary Context
This $227K-$474K 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 Indeed, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($350K) sits 60% above the category median. Disclosed range: $227K to $474K.
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.
Indeed AI Hiring
Indeed has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Remote, US. Compensation range: $245K - $474K.
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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