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About This Role
Location
US Remote
Employment Type
Full time
Location Type
Remote
Department
Research & Development
Compensation
- $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to the terms and conditions of the underlying plans or programs, as applicable and as may be amended, terminated, or superseded from time to time.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
AI Engineer – Trust & Explainability (AI Platform)
Job Summary
The AI Platform team is building the shared runtime that every AI agent at MeridianLink will run on as part of the MeridianLink One platform: model gateway, orchestration, memory, and observability, delivered as reusable services so product teams build agents once and run them safely for hundreds of credit union and lender customers. This role builds the trust and explainability layer of that runtime: the tracing, evaluation, and explanation capabilities that let engineers understand exactly what a multi-agent workflow did and why, and that let product teams show end customers the right level of explanation to trust an agent's output.
This role has a strong dotted-line relationship to MeridianLink's Security Operations team, so the platform's trust guarantees and Security's threat models are built from the same picture.
About the Opportunity
This is a deeply technical, hands-on engineering role and an integral part of the platform itself. You will write the code that traces agent actions across a multi-agent workflow, adopt and extend leading open-source explainability and observability frameworks, and build the primitives product teams use to surface explanations to lenders, credit unions, and their borrowers. You should have real hands-on experience with LLM-based systems and a strong pull toward the problems of trust, tracing, and evaluation. You do not need to be an expert in all of it. This is a place to build that expertise while the platform is being built, working alongside a Staff engineer team lead and architects who will invest in your growth.
What it means to be an L2 AI Engineer at MeridianLink
L2 AI Engineers handle a broad range of work independently, from bug fixes to feature development. They have developed a specialization and are deepening it. They write code that other engineers trust, ask good questions, and are starting to help the engineers around them. L2 AI Engineers at MeridianLink are active, daily users of AI-assisted development tools.
Technical Execution & Delivery
- Completes assigned features and bug fixes independently, with limited need for day-to-day guidance
- Designs components within a well-defined scope; escalates questions on complex system design rather than guessing
- Participates in code review and provides constructive feedback
- Surfaces blockers proactively rather than waiting for check-ins
Craft & Professionalism
- Writes tests that cover the functionality they ship
- Monitors and responds to issues with their own work
- Documents decisions and implementation details that others will need later
Agent Tracing & Explainability
- Understands how to instrument an agent so a single conversation can be followed from the first user message through every model call, tool call, retrieval, agent handoff, and decision to the final response
- Builds the correlation that ties actions across multiple agents in one workflow into a single readable trace
- Turns raw trace data into an explanation a human can follow: what the agent did, what it relied on, and why it chose that path
Customer-Facing Trust & Explanation
- Understands that the explanation an engineer needs and the explanation a borrower or loan officer needs are different, and builds for both
- Works with product engineers on what an agent should disclose at the product surface: what it did, what data it used, how confident it was, and what a human should verify
- Applies judgment about the right level of explanation for a regulated lending and account-opening context
Trust & Explainability Frameworks
- Familiar with the open-source landscape for LLM observability, tracing, and evaluation, and can evaluate a framework against the platform's needs
- Integrates and extends existing frameworks rather than rebuilding them, and builds what does not exist yet
- Keeps the platform's instrumentation aligned to emerging standards so traces stay portable across tools
Agent Evaluation & Quality
- Understands how to measure the quality of non-deterministic output: golden datasets, rubric and LLM-as-judge scoring, and regression baselines
- Builds evaluation checks that run in CI so a model swap, prompt change, or tool change is tested before it reaches customers
- Treats evaluation data as versioned, reviewed code
Safety & Tenant Isolation
- Knows the common attack patterns against LLM applications (prompt injection, jailbreaks, tool misuse, data exfiltration) and how to write tests for them
- Understands tenant isolation as a platform guarantee and builds tests that prove one customer's agent can never see another customer's data
- Contributes to the shared guardrail layer so every agent inherits protection without re-implementing it
What Success Looks Like
In the first few months, a successful hire has shipped tracing that follows a full agent workflow end to end, readable by an engineer who did not write it, and has contributed working components to the platform's evaluation harness. Over the first year, success looks like this: a first version of the customer-facing explanation primitive is live in at least one product, evaluation and isolation checks run automatically on every agent release, and when an engineer asks why an agent did something, or a customer asks whether to trust it, the platform answers. Engineers who thrive here like the intersection of AI and rigor, want to become the team's go-to on agent tracing and explainability, and are motivated by making AI something people can see into and trust.
Key Responsibilities
Multi-Agent Tracing & Explainability
- Build tracing across the platform's gateway, orchestration, memory, and tool layers, following defined designs from the team lead and architects
- Build the correlation that links actions across agents in a single multi-agent workflow, including handoffs, parallel branches, and retries
- Build the developer-facing trace view that makes a full agent conversation readable to someone who did not write the agent
- Build the explanation layer that turns trace data into a human-readable account of why an agent did what it did
Customer-Facing Trust
- Build the platform primitives product teams surface to end users: explanation records, confidence and provenance metadata, and "what the agent relied on" summaries
- Partner with product engineers on the Document Request Agent and the MLM agents to land those primitives in real products
- Iterate on the explanation format based on feedback from product teams and customer-facing staff
Trust & Explainability Frameworks
- Evaluate and integrate open-source observability, tracing, and evaluation frameworks into the platform runtime
- Extend those frameworks where our agent workloads need more than they offer, and contribute fixes and extensions back upstream where it makes sense
- Build the gaps: the pieces of trust and explainability tooling the ecosystem does not provide yet
Evaluation Tooling
- Build and maintain components of the platform's evaluation framework: golden dataset management, test runners, scoring pipelines, and regression reporting
- Build the tooling that helps teams create and version golden datasets from de-identified real traffic and synthetic cases
- Run model and prompt comparisons for the platform's shared components and report what changed
Safety & Isolation Testing
- Build red-team and adversarial test suites covering prompt injection, jailbreak attempts, tool misuse, and data exfiltration, and run them in the platform's release process
- Build the automated test suite that proves agents on the shared runtime cannot cross tenant boundaries through memory, retrieval, tool calls, or model context
- Share red-team findings and new attack patterns with Security Operations and pull their threat models into the platform's tests
Collaboration & Growing Others
- Participate in design discussions and code reviews; give and receive feedback constructively
- Support onboarding of L1 AI Engineer teammates; share context and help them get unblocked
- Contribute to documentation that reduces tribal knowledge on the team
Qualifications
Required Experience
- 3+ years of professional software engineering experience, delivering features independently in a production environment
- Solid understanding of algorithms, data structures, and software design fundamentals
- Proficiency with standard development tooling: Git, Docker, automated testing, and modern scripting languages
- Active daily use of AI-assisted development tools
- Bachelor's degree in Computer Science, Software Engineering, or equivalent experience
- Hands-on experience building software that integrates large language models (LLM APIs, agent frameworks, RAG pipelines, or similar), in production or in substantial personal or open-source projects
- Proficiency in Python with production experience; TypeScript a plus
- Hands-on experience in at least one of the following, with real interest in growing into the others: LLM observability and tracing, LLM evaluation and testing, agent frameworks and multi-agent orchestration, or application security testing
- Experience with distributed tracing or observability tooling in a production system
- Strong automated testing instincts, including an interest in how to test systems that do not return the same answer twice
- Experience running workloads on Azure or AWS, including the basics of identity and access management, networking, and secrets management
Preferred Qualifications
- Experience with OpenTelemetry, including the GenAI semantic conventions, or OpenLLMetry
- Experience with LLM observability and evaluation tools (Langfuse, Arize Phoenix, LangSmith, Braintrust, promptfoo, DeepEval, or equivalent)
- Contributions to open-source AI observability, evaluation, or agent framework projects
- Experience with cloud-managed model services such as AWS Bedrock or Azure OpenAI
- Experience building or operating multi-tenant SaaS systems where tenant isolation was a hard requirement
- Prior experience in financial services, fintech, or another regulated industry where explainability shaped technical decisions
- Experience building developer-facing debugging or visualization tools
Compensation Range: $104,148 - $177,600 Apply for this Job
Salary Context
This $104K-$177K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($140K) sits 34% below the category median. Disclosed range: $104K to $177K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
MeridianLink AI Hiring
MeridianLink has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Based in Remote, US. Compensation range: $177K - $177K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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