Interested in this AI/ML Engineer role at Origami Risk LLC?
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Overview:
The AI Product Engineer is responsible for accelerating how Origami designs, assembles, and ships user\-facing product experiences on the Origami Risk platform. This role focuses on low/no\-code and AI\-assisted solutions for technology\-literate systems thinkers who understand services architecture, authentication, security, and integration patterns and can use AI tools to orchestrate end\-to\-end solutions quickly and safely. This person works directly with business and client stakeholders to turn outcomes and constraints into working product experiences, while partnering with Product, Architecture, DevOps, Security, and QA to ensure quality, compliance, and maintainability.
Starting base pay for this role is between $117,000 and $145,000\. The actual base pay is dependent upon many factors, such as transferable skills, work experience, business needs, training, location, and market demands. The base pay range is subject to change and may be modified in the future. This role will be eligible for a bonus as well as competitive medical, dental, and vision benefits, wellness reimbursement, life insurance, and a 401(k) with company match. We offer vacation and sick leave benefits (under a flexible time off policy in most states).
Responsibilities:
- Translates business problems and user needs into shippable experiences and modules aligned to OKRs and ROI.
- Uses tools like Replit, ClaudeCode, and Cursor to draft, synthesize, and assemble functional components, integration glue, and configuration—without being a traditional software developer.
- Integrates front‑end experiences with Origami Risk platform services (e.g., data, workflow, rules, reporting, notifications, external connectors) using approved patterns and APIs.
- Applies SSO/OIDC/OAuth patterns, secrets management, role/permission models, data privacy, and auditability from day one.
- Partners with Architecture to choose patterns, ensure scalability/resilience, and align with reference architectures and guardrails.
- Iterates quickly with stakeholders and users; harden prototypes into maintainable production experiences with clear documentation and handoff plans.
- Collaborates with QA to specify acceptance criteria, test data, and automated checks (vulnerability scans, accessibility, basic UI tests).
- Designs features for telemetry (usage, performance, errors), defines runbooks, and ensures product health metrics are visible post‑launch.
- Adheres to regulatory and internal controls (e.g., SOC 2, HIPAA/PHI, PCI, where applicable), data handling policies, and secure SDLC practices.
- Maintains concise specs, PRDs\-lite, architecture notes, and user‑facing release notes.
- Utilizes AI tools, platform capabilities, and approved patterns to assemble and ship user experiences and integrations at speed.
- Leverages AI to generate code and configurations as needed to ensure solutions meet standards and integrate correctly.
Qualifications:
- Bachelor’s degree in Information Technology, Computer Science, or related field.
- 5\+ years in product‑adjacent, solution engineering, systems analysis, technical consulting, or platform configuration roles.
- Demonstrable systems thinking: understanding of services architecture, APIs, auth (OAuth/OIDC/SSO), data flows, and integration patterns.
- Hands‑on experience assembling solutions via low/no‑code, configuration, or AI‑assisted coding tools (Replit, ClaudeCode, Cursor, or similar).
- Strong product sense and UX empathy; ability to translate problems into user journeys and working UI.
- Working knowledge of security and privacy principles (least privilege, data minimization, PII/PHI handling, secrets management).
- Excellent written and verbal communication, with crisp documentation and stakeholder facilitation skills.
*Preferred Qualifications** Familiarity with Origami Risk platform services or similar enterprise platforms (policy, claims, risk, workflow, rules engines).
- Exposure to front‑end frameworks (React/Vue) at the conceptual level—enough to guide AI tools and evaluate output.
- Experience with API gateways, eventing, webhooks, and ETL/ELT concepts.
- Knowledge of accessibility (WCAG) and design systems.
- Experience in regulated environments (SOC 2, PCI, HIPAA, ISO 27001, GDPR).
Benefits:
- Medical and Dental coverage available for employees, dependents, domestic partners, and spouses
- Paid Time Off – Flexible options plus 10 paid company holidays where available\*\*
- All full\-time positions are hybrid, with many eligible to be completely remote
- Fully Paid by Origami Risk – Vision insurance, Short \& Long\-Term Disability Insurance, and Basic Life Insurance
- Generous family leave options—including adoption and foster care placements
- Pre\-Tax Savings Accounts – Flexible Spending Account, Health Savings Account, Commuter Benefits, Dependent Care Savings Account
- Retirement Savings – 401(k) with company match up to 4%
- Employee Assistance Program (EAP) – Confidential \& Free support offered to colleagues facing personal or work\-related complications
- Education Assistance Program – to help colleagues pursue industry/role\-specific certifications
- Wellness Benefits – reimbursement program to invest in healthy habits as well as support better colleague productivity and stress management
- Additional coverages available – Pet Insurance, Critical Illness Insurance, and Voluntary Life \& AD\&D coverage
*\*\*Flexible PTO not available in California or the UK*
Who We Are:
Origami Risk delivers single\-platform SaaS solutions that help organizations best navigate the complexities of risk, insurance, compliance, and safety management.
Founded by industry veterans who recognized the need for risk management technology that was more configurable, intuitive, and scalable, Origami continues to add to its innovative product offerings for managing both insurable and uninsurable risk; facilitating compliance; improving safety; and helping insurers, MGAs, TPAs, and brokers provide enhanced services that drive results.
A singular focus on client success underlies Origami’s approach to developing, implementing, and supporting our award\-winning software solutions. *Origami Risk is proud to be an equal opportunity employer. We thrive and benefit from diversity and are committed to creating an inclusive and equitable environment for all employees. We do not discriminate against any individual based upon race, religion, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, color, sex, national origin, age, marital status, military or veteran status, disability, or any other characteristic protected by applicable law.* *Caution: Be alert to recruiting scams. We have received reports of individuals impersonating Origami Risk recruiters to deceive candidates into disclosing personal information. These impostors use fake Origami Risk domain names and email addresses. Please double\-check that any email address from an Origami Risk recruiter ends with* *origamirisk.com* *or* *talent.icims.com. And to confirm the legitimacy of any recruiting communication, feel free to email* *[email protected]**.*
Salary Context
This $117K-$145K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Origami Risk LLC, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($131K) sits 39% below the category median. Disclosed range: $117K to $145K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Origami Risk LLC AI Hiring
Origami Risk LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $145K - $145K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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