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About This Role
#### About us
Coalition is the world's first Active Insurance provider designed to help prevent digital risk before it strikes. Founded in 2017, Coalition combines comprehensive insurance coverage and innovative cybersecurity tools to help businesses manage and mitigate potential cyberattacks.
Opportunities to make an impact with bold thinking are real—and happening daily at Coalition.
#### About the role
Coalition's machine learning models are only as good as the data they're trained on. This role exists to make sure that data is right.
You'll own labeling quality and methodology across Coalition — designing annotation tasks, defining quality frameworks, and ensuring every labeled dataset meets the standard required to ship production ML models. You'll manage our labeling platform (Label Studio), work directly with ML and product teams to structure labeling programs, and oversee outsourced labeling vendors to hit quality and throughput targets.
This role reports to the Chief Product Officer and sits at the intersection of product, ML, and operations. You won't manage internal labelers — all annotation work is outsourced — but you will be the single point of accountability for whether Coalition's labeled data is accurate, consistent, and fit for purpose.
#### Responsibilities
- Labeling quality \& methodology: Define annotation guidelines, taxonomies, and edge\-case protocols for each labeling program. Establish gold standard datasets, inter\-annotator agreement (IAA) targets, and audit sampling processes. Identify and remediate mislabeled data in existing datasets.
- Platform \& tooling: Serve as the primary user and requirements driver for Label Studio — defining project configuration needs, workflow designs, pre\-labeling pipeline requirements, and integration points with ML infrastructure. Partner with the data engineering team that builds and maintains the platform.
- Cross\-functional partnership: Work with ML engineers, data scientists, and product managers to translate model requirements into well\-structured labeling tasks. Challenge teams on task design when labeling instructions are ambiguous or likely to produce unreliable labels.
- Vendor management: Source, onboard, and manage external labeling vendors and BPOs in coordination with Coalition's operations team. Set quality SLAs, run calibration sessions, and manage feedback loops to labelers. Hold vendors accountable to accuracy, not just throughput.
- Measurement \& improvement: Define and track operational metrics — label accuracy, IAA scores, cost per label, turnaround time — and use them to drive continuous improvement. Identify opportunities for active learning, model\-assisted labeling, and pre\-annotation to reduce cost without sacrificing quality.
#### Skills and Qualifications
- 5\+ years in ML data operations, data labeling, or a related field (ML engineering, data science, or data engineering with heavy labeling exposure)
- Deep understanding of annotation quality frameworks: IAA, consensus labeling, gold standard evaluation, error taxonomy, and calibration workflows
- Direct experience managing labeling platforms (Label Studio strongly preferred; Scale AI, Labelbox, Prodigy, or similar acceptable)
- Track record managing outsourced labeling vendors or BPOs for ML data production
- Familiarity with common ML labeling tasks: text classification, NER, document extraction, intent detection
- Comfortable working in Python and SQL; bonus if you've built tooling around labeling workflows or quality measurement
- Strong opinions on what makes labeled data good or bad, and the willingness to push back when it's bad
- Experience in insurance, cybersecurity, or fintech is a plus but not required
#### Compensation
Our compensation reflects the cost of labor across several US geographic markets. The US base salary for this position ranges from $134,400/year in our lowest geographic market up to $200,000/year in our highest geographic market. Consistent with applicable laws, an employee's pay within this range is based on a number of factors, which include but are not limited to relevant education, skills, job\-related knowledge, qualifications, work experience, credentials, and/or geographic location. Your recruiter can share more on target salary for your location during the interview process. Coalition, Inc. reserves the right to modify this range as needed.
#### Perks
- 100% medical, dental and vision coverage
- Flexible PTO policy
- Annual home office stipend and WeWork access
- Mental \& physical health wellness programs (One Medical, Headspace, Wellhub, and more)!
- Competitive compensation and opportunity for advancement
#### Why Coalition?
Work at Coalition is centered on the joint mission to Protect the Unprotected. We have built a remote\-first, highly inclusive culture that welcomes people from diverse backgrounds. We trust each other to take responsibility, share ownership of outcomes, and put in the work together to protect businesses from digital risk. Coalition's exceptional growth stems from its ability to address real\-world problems for organizations of all sizes while remaining true to our founding values of character, humility, responsibility, purpose, authenticity, and inclusion.
We're always looking for collaborative, inquisitive individuals to join \#OurCoalition.
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#### Privacy Notice
Coalition is committed to protecting your privacy and handling your personal information responsibly. We collect, use, and store personal information as necessary for the recruitment process and in compliance with applicable privacy laws and regulations in all regions where we operate. We want you to understand what personal information we collect, how we use it, and your rights regarding access, correction, and deletion of your data where applicable. Information submitted, collected, and processed as part of your application is subject to Coalition's Privacy Policy. For further details, please review our full Privacy Policy or contact us with any questions regarding how your information is handled.
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#### Safe Hiring Notice
All legitimate communication from Coalition comes from @coalitioninc.com emails, and open roles are listed only on our Careers page. We never ask for payment, banking details, or personal identification before an offer is accepted through our secure systems. If you believe you've been a victim of fraudulent recruiting, follow guidance from the Federal Trade Commission (FTC).
#### Anti\-Discrimination Notice
Coalition is proud to be an Equal Opportunity employer. Our policy is to provide equal employment opportunities to all individuals, without discrimination or harassment on the basis of any characteristic protected by applicable laws in each country where we operate. This commitment includes, but is not limited to, ensuring equal treatment in recruitment, selection, training, promotion, transfer, compensation, and all other aspects of employment. Coalition does not tolerate discrimination or harassment of any kind, and we are dedicated to fostering an inclusive and supportive workplace.
#### Accommodations
Coalition is committed to providing reasonable accommodations to qualified individuals with disabilities, including applicants and employees, in accordance with applicable laws and regulations in each country where we operate. Our policy is to support equal opportunity in the hiring process by considering qualified applicants regardless of disability or other protected characteristics, unless providing accommodation would impose an undue hardship or disproportionate burden. If you require accommodation to complete an application, interview, pre\-employment testing, or participate in the selection process, please contact us at [email protected]. We also consider all qualified applicants, including those with criminal histories, in line with applicable laws and regulations in each jurisdiction.
To all recruitment agencies: Coalition does not accept unsolicited agency resumes. Do not forward resumes to our email alias, employees, or other physical or virtual organization locations. Coalition is not responsible for any fees related to unsolicited resumes.
Salary Context
This $134K-$200K range is below the median 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 Coalition Inc., 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($167K) sits 24% below the category median. Disclosed range: $134K to $200K.
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.
Coalition Inc. AI Hiring
Coalition Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $200K - $200K.
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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