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About This Role
About Ceribell
Ceribell is a medical technology company focused on transforming the diagnosis and management of patients with serious neurological conditions. The Ceribell System is a novel, point\-of\-care electroencephalography ("EEG") platform specifically designed to address the unmet needs of patients in the acute care setting, and is being used in hundreds of community hospitals, large academic facilities and major IDN's across the country. Our entire team is driven by a shared commitment to transforming the landscape of critical care through our rapid seizure detection technology, come join the movement!
Position Overview:
The AI \& Data Systems Engineer will be a key individual contributor on Ceribell's Data Architecture \& Engineering team, responsible for much of the hands\-on implementation work that turns architectural plans into reality. This role exists at the intersection of software development, data engineering, and DevOps as someone who is equally comfortable writing application code and managing the infrastructure those systems run on.
A significant part of this role involves taking ideas and early\-stage prototypes for AI\-powered internal tools and engineering them into stable, supportable internal applications. The ideal candidate is a versatile builder who can move fluidly across layers of the stack, understands when something is production\-ready versus when it needs to be rebuilt properly, and takes pride in writing code and infrastructure that others can maintain and build on.
What you'll do:
- Implement internal tools and applications based on architectural direction from the Director of Data Architecture \& Engineering, including taking stakeholder prototypes and re\-engineering them into production\-grade systems.
- Manage and maintain the infrastructure supporting internal AI tools and data systems, including deployment, configuration, monitoring, and incident response.
- Write clean, well\-documented code across whatever languages and frameworks the work requires; apply sound engineering practices around testing, version control, and code review.
- Evaluate and integrate AI tools and third\-party services into internal workflows where they provide clear value, with attention to security, cost, and long\-term maintainability.
- Contribute to data quality practices: implementing automated checks, investigating pipeline failures, and helping establish clear data ownership and lineage.
- Collaborate with stakeholders across the business to understand requirements, surface technical tradeoffs, and deliver solutions that meet actual needs rather than assumed ones.
- Support access control and permissions management across systems and tooling, contributing to the team's broader security and governance practices.
- Maintain thorough documentation of systems, data flows, and processes so that institutional knowledge is preserved and accessible.
- Other responsibilities as assigned by your Manager/Supervisor
What We're Looking For:
- 3 \- 6 years of hands\-on experience in a technical role spanning some combination of software development, data engineering, and infrastructure or DevOps work.
- Experience implementing and deploying AI solutions in a production environment, including model integration, API usage, and operational maintenance.
- Proficiency in at least one general\-purpose programming language (e.g. Python) and comfort picking up new languages or frameworks as the work demands.
- Experience building and maintaining data pipelines, including working with APIs, relational databases, and cloud data platforms.
- Working knowledge of DevOps practices and tools: CI/CD pipelines, containerization (Docker), cloud infrastructure (AWS, GCP, or Azure), and infrastructure\-as\-code concepts.
- Demonstrated ability to read and understand existing codebases, including prototypes or AI\-generated code, assess their quality, and refactor or rebuild them as appropriate.
- Familiarity with enterprise business systems such as Snowflake, Salesforce, NetSuite, or similar platforms, including working with their APIs and data models.
- Strong attention to detail.
- Good written and verbal communication skills; able to explain technical decisions clearly to non\-technical colleagues.
- Bachelor's degree in Computer Science, Engineering, or a related field preferred; equivalent practical experience accepted.
Compensation:
San Francisco Bay Area, Los Angeles, and New York City Metropolitan Locations: $161K \- $173K
All other National Locations: $141K \- $162K
A candidate's final salary offer will be based on their skills, education, work location and experience, and thus it may differ from the posted range. Compensation may also include bonuses consistent with Ceribell's corporate compensation plan. Note, the above description is not all\-encompassing and Ceribell reserves the right to change or modify job duties and assignments at any time.
In addition to your base compensation, Ceribell offers eligible employees the following:
- Performance\-based incentive compensation (varies by role)
- Equity opportunities
- 100% Employer paid Health Benefits for Employees
- 50% \- 70% Employer paid Health, Dental \& Vision for dependents (depending on plan selection)
- 100% paid Life and Long\-Term Disability Insurance
- 401(k) with a generous company match
- Employee Stock Purchase Plan (ESPP) with a discount
- Monthly cell phone stipend
- Flexible paid time off
- 13 Paid Holidays \+ 3 Company Wellness Days
- Excellent parental leave policy
- Fantastic culture with tremendous career advancement opportunities
- Joining a mission\-minded organization!
Application Deadline: Ongoing
Other Job Details
Ceribell reports transfers of value to health care providers (HCPs) as required by federal and state transparency laws. These laws and implementing regulations require Ceribell to provide government agencies with HCPs' names, addresses and the type of payments or other value received, generally for public disclosure. If you are an HCP and we pay or reimburse your recruiting expenses as a result of interviewing with Ceribell, your name, address and the amount of payments made may be reported to the government.
Equal Opportunity Employer
Ceribell is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, childbirth and related medical conditions), sexual orientation, gender identity or expression, national origin, age, marital status, disability, veteran status or any other characteristic protected by law. Ceribell complies with all applicable national, state and local laws governing nondiscrimination in employment as well as work authorization and employment eligibility verification requirements of the Immigration and Nationality Act and IRCA. Ceribell is an E\-Verify employer. Any applicant with a disability who requires an accommodation during the application process should contact [email protected] to request reasonable accommodation.
Privacy Statement
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Compliance Disclaimer
If you believe this job posting is non\-compliant, please submit a report to [email protected]. Please note that we will not respond to inquiries unrelated to job posting compliance.
Salary Context
This $141K-$173K range is below 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 Ceribell, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($157K) sits 12% below the category median. Disclosed range: $141K to $173K.
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.
Ceribell, Inc AI Hiring
Ceribell, Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $173K - $173K.
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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