Interested in this AI/ML Engineer role at Dext Capital?
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About This Role
Dext Capital is a National, innovative, financial services company headquartered in the Portland Metro area. Dext offers an exceptional culture and countless opportunities for growth and advancement. We are seeking a motivated individual to join us as an AI Engineer – End User. The ideal candidate will have demonstrated experience building with the Anthropic API, Claude Cowork, Claude Skills, Connections, and Plugins, paired with a track record of rolling AI out successfully inside a corporate environment.
This position will collaborate closely with departments across Dext’s corporate team including Credit, Collections, Accounting, Marketing, Legal, and HR, to identify manual, repetitive, or routinized processes and turn them into AI-driven workflows. Reporting to the Chief Innovation Officer as a member of the IT team, this role owns Dext’s day-to-day AI enablement program, building fluency and adoption department by department.
We are looking for someone who is eager to implement AI solutions in a real-world business environment. This role will work closely with the technology team to develop, test, and support AI tools and applications while learning best practices around security, privacy, and responsible AI. A strong interest in AI, technology, and problem-solving is essential; experience in financial services is a plus.
Dext Capital has been named one of the top 5 fastest-growing companies in the Greater Northwest and a best place to work award recipient for the last 3 years. Dext offers strong benefits and is committed to leveraging technology while enabling our employees to grow in a high-performance environment
Job Duties/Responsibilities:
- Work with departments such as Credit, Collections, Accounting, Marketing, Legal, and HR to understand their manual processes and find opportunities for AI automation
- Turn business processes into working AI automations and help build a pipeline of future use cases with department leaders
- Help design, build, and maintain reusable Claude Skills, Connections, and Plugins that departments can use directly, working alongside internal teams and leadership.
- Connect AI workflows to Salesforce using tools such as Agentforce and the Salesforce plugin and skills for Claude
- Lead AI use cases from gathering requirements and building pilots to tracking results and supporting rollout
- Track results such as time saved, accuracy, and throughput for each AI tool in use
- Partner with IT and Compliance to make sure AI tools meet Dext’s data security and customer privacy standards, and help keep an inventory of AI tools in use
- Train employees on new AI tools and create simple guides and documentation for workflows.
- Provide ongoing support and improvements as department needs change
- Stay current on new Claude and AI capabilities and share ideas for how they can help Dext
Characteristics:
- Practical problem-solver — motivated to learn by doing and to see AI tools make a real difference for the teams they support
- Comfortable working independently across multiple departments with differing priorities and technical maturity
- Security- and privacy-minded by default, especially with customer and financial data
- Clear communicator who can make AI concepts accessible to non-technical staff
- Curious and continuously learning — tracks new AI capabilities and evaluates their fit for Dext
Experience Requirements:
- Bachelor’s degree in Computer Science, Data Science, Information Systems, Engineering, Business Analytics, or a related field
- Experience with AI, either practical or academic — for example, coursework, capstone or research projects, internships, or personal projects building with large language models, prompt engineering, or AI-driven automation
- Foundational understanding of AI/LLM capabilities, limitations, and appropriate use cases
- Familiarity with, or a strong interest in learning, Claude (Cowork, Skills, Connections, and Plugins), Salesforce, or comparable AI and business platforms
- Awareness of the importance of data security and customer privacy when working with AI tools
- Strong communication skills; comfortable working with non-technical staff to understand how they work and where AI can help
- Preferred: Some familiarity with finance, banking, or financial institutions.
- Preferred: Exposure to business systems such as Salesforce, Microsoft 365/SharePoint, or Azure
The pay range for this role is:
42,000 - 55,000 USD per year(Headquarters)
Salary Context
This $42K-$55K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Dext Capital, 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 11,866 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408. This role's midpoint ($48K) sits 77% below the category median. Disclosed range: $42K to $55K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Dext Capital AI Hiring
Dext Capital has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Lake Oswego, OR, US. Compensation range: $55K - $55K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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