Interested in this AI/ML Engineer role at MeridianLink?
Apply Now →Skills & Technologies
About This Role
Location
------------
US Remote
Employment Type
-------------------
Full time
Location Type
-----------------
Remote
Department
--------------
Research \& Development
Compensation
----------------
- $94,667 – $175,000
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to the terms and conditions of the underlying plans or programs, as applicable and as may be amended, terminated, or superseded from time to time.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
About the Role
We’re looking for a Sr. Technical Program Manager (TPM) to drive delivery for complex, cross\-functional initiatives—including those involving AI/ML and intelligent automation. You’ll balance scope, timeline, and dependencies while partnering across Engineering, Product, Data, Security, and Operations to deliver high\-quality outcomes. This role is ideal for someone who can run tight execution rhythms and understand the unique delivery needs of AI work (experimentation, data readiness, evaluation, governance, and iteration) in the rollout of the AI\-Driven SDLC (AIDLC),
What You’ll Do
- Own end\-to\-end project execution: plan, schedule, and drive delivery across the full project lifecycle, balancing constraints and keeping work moving forward in partnership with product and engineering counterparts.
- Build and maintain project plans, track milestones, and proactively manage risks, issues, and tradeoffs—providing clear stakeholder updates with recommended solutions.
- Serve as the PMO lead for the ML's agentic platform and customer facing products — the structured methodology for developing, testing, deploying, and operating AI agents within MeridianLink’s agentic architecture.
- Partner with engineering leads to embed AI into each SDLC phase: requirements (AI\-assisted grooming agents), development (co\-pilot and Claude Code standards), testing (test\-driven development metrics), and release (zero\-downtime deployment, feature flag governance).
- Establish and continuously improve team processes that increase quality and productivity through process definition, education, and refinement.
- Facilitate light weight agile ceremonies (planning, standups, retrospectives) and drive continuous improvement through retrospective assessments.
- Use tools such as Jira (and related reporting) to support teams and enable visibility into progress, capacity, and delivery health. Bridge product and engineering AI alignment.
What We’re Looking For (Required Qualifications)
- 7\+ years of technical program management in a software engineering organization, with at least 2 years in an AI, ML, or platform transformation context.
- Demonstrated experience managing AI/ML initiatives or SDLC modernization programs — not just awareness, but delivery ownership.
- Fluency with AI\-driven SDLC concepts: AI\-assisted requirements, co\-pilot/code generation tooling, test automation, agentic deployment, and observability.
- Ability to translate engineering complexity into executive narratives and vice versa; strong stakeholder management across CTO, VPs, and ICs.
- Experience running programs that span multiple scrum teams, including offshore\-heavy organizations.
- Hands\-on with program/project tooling (Jira, Confluence, or equivalents) and familiarity with engineering metrics (DORA, SPACE, or similar frameworks).
What Success Looks Like (First 90 Days)
- Delivery plans are clear, realistic, and transparent; stakeholders consistently know status, risks, and next steps.
- Dependencies are surfaced early and managed actively; team execution rhythms are consistent and effective.
- AI\-related initiatives have strong delivery hygiene (milestones, measurable outcomes, and aligned execution across data/Product/Engineering).
Why This Role
You’ll play a pivotal role in delivering cross\-functional technology initiatives—and help the organization execute confidently as AI becomes a more central part of products and operations. You’ll bring clarity, momentum, and structure to complex work while partnering with leaders across Engineering, Product, and Data.
About MeridianLink
MeridianLink is a leading provider of cloud\-based software solutions for financial institutions, serving \~1,500 lenders with a loan origination platform deeply embedded in their core lending processes. With 400\+ integrations and multi\-year customer relationships, MeridianLink combines mission\-critical workflow depth with a growing AI\-native product roadmap. The company is backed by a board Tech Committee with deep fintech and AI expertise.
Compensation Range: $94,667 \- $175,000
Apply for this Job
Salary Context
This $94K-$175K range is in the lower quartile 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 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 $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 ($134K) sits 38% below the category median. Disclosed range: $94K to $175K.
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.
MeridianLink AI Hiring
MeridianLink has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $175K - $175K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.