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About This Role
Location
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US Remote
Employment Type
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Full time
Location Type
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Remote
Department
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Research \& Development
Compensation
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- $104,148 – $177,600
MeridianLink runs a comprehensive background check, credit check, and drug test as part of our offer process.
It is not typical for offers to be made at or near the top of the salary range. The actual salary will be determined based on experience and other job\-related factors permitted by law including geographical location.
Meridianlink offers:
- Insurance coverage (medical, dental, vision, life, and disability)
- Flexible paid time off
- Paid holidays
- 401(k) plan with company match
- Remote work
All compensation and benefits are subject to the terms and conditions of the underlying plans or programs, as applicable and as may be amended, terminated, or superseded from time to time.
\#LI\-REMOTE
This AI Engineer II is a mid\-level engineering role responsible for designing, developing, testing, and maintaining AI platform components that enable MeridianLink's products to deliver AI\-powered features consistently and reliably. You will work within the AI platform team to implement services such as model integration layers, retrieval\-augmented generation infrastructure, prompt management systems, and evaluation pipelines. This role collaborates closely with Senior and Staff engineers on architecture, works directly with product engineering teams to understand their AI integration needs, and helps establish the standards and patterns teams follow when building on top of the AI platform.
WHAT YOU'LL DO
Core Platform Development
- Implement AI platform components — including model serving layers, retrieval infrastructure, prompt management, and output evaluation services — under the guidance of Senior and Staff engineers
- Develop and maintain APIs that product teams use to integrate AI capabilities into their applications
- Build observability and monitoring into platform services to track output quality, latency, cost, and system health
- Contribute to design discussions and provide input on trade\-offs between simplicity, reliability, and scalability
Integration \& Collaboration
- Work with product engineering teams to understand how they want to use AI capabilities and translate requirements into platform features
- Participate in technical design sessions and code reviews with product teams building on the AI platform
- Help identify patterns where multiple product teams are solving the same AI integration problem and consolidate that work
Standards, Documentation \& Knowledge Sharing
- Contribute to documentation, reference implementations, and runbooks that help product teams adopt AI platform services
- Participate in defining and enforcing integration standards — API contracts, error handling patterns, cost attribution
- Mentor junior engineers (Engineer I) and help them grow their understanding of AI systems and platform design
AI Reliability \& Safety
- Implement evaluation and monitoring pipelines that give teams visibility into AI output quality and model behavior
- Contribute to content safety standards and compliance guardrails appropriate for a regulated financial services environment
- Help design systems that properly handle PII, maintain audit trails, and meet data residency requirements in AI pipelines
REQUIRED QUALIFICATIONS
- 3–5 years of professional software engineering experience
- Strong proficiency in backend engineering using Python, C\#/.NET, Java, or Node.js
- Solid understanding of algorithms, data structures, and system design principles
- Hands\-on experience integrating large language models or ML models into production applications
- Working knowledge of retrieval\-augmented generation (RAG) concepts, vector databases, and embedding pipelines
- Experience building or contributing to shared services or platform components
- Familiarity with cloud\-managed AI services (AWS Bedrock, Azure OpenAI, or equivalent)
- Experience with APIs, asynchronous processing, and event\-driven architectures
- Bachelor's degree in Computer Science, Software Engineering, or equivalent professional experience
PREFERRED QUALIFICATIONS
- Experience with LLM frameworks (LangChain, LlamaIndex, etc.) or prompt engineering
- Familiarity with vector databases (Pinecone, Weaviate, Milvus, or similar)
- Knowledge of containerization and orchestration (Docker, Kubernetes)
- Experience with observability tools and designing monitoring for ML/AI systems
- Prior work in regulated industries with data handling, compliance, or audit requirements
- Experience with fintech, banking, or other financial services environments
- Active daily use of AI\-assisted development tools and understanding of their benefits and limitations
TECHNICAL SKILLS
- In\-depth knowledge of one or more programming languages (Python, C\#, Java, Node.js, etc.)
- Strong understanding of RESTful API design and web service architecture
- Proficiency with SQL and relational databases
- Experience with version control (Git) and CI/CD pipelines
- Ability to write well\-tested, maintainable code and participate effectively in code reviews
YOUR GROWTH PATH
As an AI Platform Engineer II, you'll deepen your expertise in AI systems and platform design. You'll take on increasingly complex components within the AI platform, participate more actively in architectural decisions with guidance from Senior engineers, and develop a strong understanding of how AI capabilities are reliably integrated across products. Your path toward Senior Engineer (L3\) involves demonstrating the ability to lead the design and implementation of significant platform components, mentor engineers effectively, and think strategically about system\-wide tradeoffs and long\-term maintainability.
WHY JOIN MERIDIANLINK
- Build the AI Platform: Work on critical infrastructure that powers AI across all of MeridianLink's products. Your work directly enables product teams to deliver AI\-powered features faster and more reliably.
- Collaborative Culture: We encourage an open, collaborative work culture where every idea is valued. As a fully remote company, we're intentional about creating meaningful opportunities to connect with your team.
- Continuous Learning: We're committed to helping you grow professionally. You'll have access to AI/ML training, job\-specific technical development, monthly soft\-skill sessions, and mentorship from experienced Senior and Staff engineers.
- Work on Meaningful Problems: Your work goes beyond code. You'll help create reliable, safe, and compliant AI systems that empower financial institutions to serve their communities better.
- Career Development: We promote from within and believe in developing our employees for long\-term growth toward Staff and Principal roles.
- Accessible Leadership: Our open\-door policy gives you direct access to executives. We want to hear your ideas and feedback.
- Work\-Life Balance: We understand that you have a full life outside work. We honor your personal commitments while maintaining productivity.
- Great Place to Work: MeridianLink is certified as a Great Place to Work® for 2026\.
Compensation Range: $104,148 \- $177,600
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Salary Context
This $104K-$177K range is below the median 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 MeridianLink, 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 $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 ($140K) sits 34% below the category median. Disclosed range: $104K to $177K.
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.
MeridianLink AI Hiring
MeridianLink has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $177K - $214K.
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.
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