Interested in this AI/ML Engineer role at ConsumerAffairs?
Apply Now →Skills & Technologies
About This Role
Note: This role is based in our Tulsa office and requires regular in\-office attendance. We're currently focusing our search on candidates who already live in the Tulsa area.
Under direction from the BDR Sales Manager, the Business Development Representative owns the top of the funnel for Aspect.
Aspect is an ecosystem, not a single product, and this role sells the whole of it. There is no separate reputation seat and marketplace seat — one team, one motion. What that means day to day is that the BDR does enough research on each brand to understand where it's losing ground, then earns a single meeting with the person who owns that problem.
The work is genuinely AI\-shaped in two directions. The pitch is about AI: how a brand shows up when a buyer asks a model instead of a search engine, and what it costs them when a competitor shows up instead. The job is also done with AI: our reps use Claude and our internal tooling to research a category, read a brand's position, and build a point of view in minutes rather than hours.
Top of funnel earns the meeting. The deeper value\-building happens with an Account Executive once there's interest.
This role suits someone who thrives in a fast\-paced environment, uses judgment over scripts, and genuinely connects with people.
Responsibilities include:
Research and prioritize. Work prospects against our ICP and scoring rubric — company size and revenue, vertical, current standing on ConsumerAffairs, who owns page one of their branded search, and whether they or a competitor appears in AI answers. Score honestly and disqualify honestly rather than padding the list.
Build a point of view before you reach out. Use AI tooling to get up to speed on a category and a brand fast enough that your first touch says something only someone who looked would say.
Run multi\-channel outbound. LinkedIn and email interleaved, short and personal rather than templated, with disciplined follow\-up.
Diagnose on the intro call. Identify what the brand is actually losing and where Aspect fits, rather than presenting a menu.
Hand off cleanly. Book meetings for Account Executives with context on the buyer and the diagnosed problem, so the AE walks in prepared
Keep the system clean. Maintain an accurate Salesforce pipeline, produce reporting from disposition data, and keep enrichment and sequencing tooling current.
Protect the list. Enforce suppression discipline on every batch — do\-not\-email and existing\- client checks run at list cut and again before send.
Close the loop. Feed outcomes back to sales and marketing leadership: which angles, verticals, and titles convert, and which don't.
Requirements
Minimum Qualifications \& Credentials
A minimum of 2 years in sales or client services experience in a high paced, technical environment
Demonstrated experience effectively managing a high volume customer pipeline
Experience and comfortability around cold emailing, cold calling and LinkedIn Outreach
Hard/Technical Skills
Experience with Google Workspace, Salesforce, and sales enablement software
Experience using B2B sales enablement tools such as SalesForce, Hubspot, Outreach, Gong etc is a plus
General knowledge of SEO and search engines
Benefits
Why You’ll Love Working Here
At ConsumerAffairs, your voice matters. We foster a collaborative environment where you’re encouraged to take initiative, experiment boldly, and grow professionally. We're committed to work\-life harmony, career development, and celebrating wins together.
Health Care Plan (Medical, Dental \& Vision)
Retirement Plan (401k)
Life Insurance (Basic, Voluntary \& AD\&D)
Paid Time Off (Vacation, Sick \& Public Holidays)
Family Leave (Maternity, Paternity)
Short Term \& Long Term Disability
Training \& Development
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 ConsumerAffairs, 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.
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.
ConsumerAffairs AI Hiring
ConsumerAffairs has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tulsa, OK, US.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.