Interested in this AI/ML Engineer role at Numerica Credit Union?
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About This Role
Description:
\*\*There is no relocation provided for this role. Selected candidate must be fully available to begin onsite within 30 days from offer. \*\*
Accepting Applications until this Thursday, August 13th at 5 p.m. Pacific Time
AI Solutions SpecialistDepartment: Digital Experience Administration
Non\-Exempt, Range 11: $32\.29 an hour to $48\.42 an hour
Report To: SVP Digital Experience \& Engagement
SUMMARY:
With the goal of enhancing lives, fulfilling dreams, and building communities this position supports our members and the Digital Team by supporting the design and implementation of AI\-powered applications and capabilities that enhance existing process, knowledge sharing, and member service delivery. Responsible for testing and maintaining Digital AI solutions once they are ready for UAT or released to production. Works collaboratively with business stakeholders and IT partners to ensure solutions deliver value and align with internal policy and AI standards.
ESSENTIAL DUTIES AND RESPONSIBILITIES include the following. Other duties may be assigned.
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- In partnership with internal stakeholders, develop, deploy and maintain AI solutions, copilots and capabilities using organizational frameworks and platforms such as Genesys, Microsoft, and/or other Numerica approved AI platforms.
- Responsible to develop evaluation framework and test AI models and solutions for accuracy, security, and usability to ensure compliance with stakeholder requirements, policies, data governance and security protocols.
- Responsible to routinely run evaluations for hallucination, response quality, and bias/PII mitigation violations on an ongoing basis.
- Maintain knowledge article ecosystem for LLM or other relevant solutions, even to the extent that knowledge articles are maintained in order to address hallucination or response quality.
- In collaboration with stakeholders, prototype and iterate on AI use cases relevant to the needs of the business (and in accordance with the platform capability roadmap).
- Conduct proof of concepts and pilots for emerging AI technologies. Document technical findings, share potential business value, risks and recommendations.
- Create user training documentation, user guides and support necessary training teams at Numerica.
- Collaborate with internal IT teams and departments and vendors to coordinate releases and ensure seamless AI deployment and maintenance. As necessary, work collaboratively with IT to develop change controls that balance different needs.
- Assist IT with tracking and reporting on initiative outcomes, performance metrics and lessons learned to demonstrate the value of the capability.
- Maintain current knowledge of AI trends, vendor roadmaps, and Numerica AI controls to inform solution design and continuous improvement of internal practice processes and efficiency.
- Maintains knowledge and adheres to all BSA regulations and compliance standards on internal and external policies.
- Effectively incorporates concepts of CARES Principles in daily behaviors and interactions:
- Connect – Create meaningful relationships
- Ask – Be intentionally curious
- Resolve – Actively seek solutions
- Elevate – Never stop improving
- Strengthen – Be the difference
SUPERVISORY RESPONSIBILITIES:
This position has no formal, direct supervisory responsibilities although will work with leaders and peers in the organization to drive results.
PERFORMANCE STANDARDS:
Contributes to the organization's mission statement and goals by providing the highest quality of service, treating each person professionally \- with warmth, courtesy, and respect, and making a personal effort to make members feel they belong and are important to the credit union.
WORKING CONDITIONS:
This position operates in a traditional office environment with standard business hours. Regular use of standard office equipment and extended periods of sitting, standing, and computer work may be required. This position requires working on\-site during regular business hours. Regular interaction with coworkers, members, and/or management through both in\-person and virtual communication platforms is required.
EDUCATION and/or EXPERIENCE:
Bachelor’s degree, preferably in Computer Science, Information Systems, or other related field required, or Associate’s degree and 1 to 2 years direct experience working with AI models and solutions, or equivalent combination of education and experience. Previous experience in financial services or digital service delivery preferred. Relevant work experience in AI, data analytics, and/or model validation preferred.
CERTIFICATES, LICENSES, REGISTRATIONS:
None
SKILLS and ABILITIES:
- Excellent communication skills with ability to translate complex technical concepts for non\-technical stakeholders
- Strong analytical skills with emphasis on data\-driven decision making
- Adapt to changing demands, multitask, and manage competing priorities
- Ability to navigate ambiguity and create clarity through learning, testing, and analysis.
Disclaimer: The job description does not imply an employment contract, nor is it intended to include every duty, task or instruction for which the employee is responsible. Other tasks may be assigned, based on business need and at Management’s request.
Learn more about our Benefits and Perks here\-
https://www.numericacu.com/globalassets/images/pdfs/employee\-benefits\-summary\-numerica.pdf
Requirements:
Numerica Credit Union is an Equal Opportunity/Affirmative Action Employer
Numerica Credit Union provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
If you are a qualified individual with a disability or a disabled veteran, you have the right to request a reasonable accommodation for purposes of participating in the application/hiring process with Numerica Credit Union. If you are unable or limited in your ability to use or access Numerica Credit Union’s career page at www.numericacu.com as a result of your disability, you can request reasonable accommodations by calling your recruiter.
Salary Context
This $66K-$99K 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 Numerica Credit Union, 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 ($83K) sits 61% below the category median. Disclosed range: $66K to $99K.
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.
Numerica Credit Union AI Hiring
Numerica Credit Union has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Spokane Valley, WA, US. Compensation range: $99K - $99K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
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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