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About This Role
Applause is raising the bar for digital quality and employee experience. Recognized as a Top Workplace, Applause provides award-winning software testing and UX research solutions to top brands. Our fully managed services leverage a global team and the world's largest independent testing community. We improve digital experiences for global innovators like Google, Microsoft, PayPal, Starbucks, Vodafone, and BMW.
AsanAI Solutions Consultant you willto be the technical backbone of our AI Evaluations sales motion. You'll partner with Account Executives to scope, design, and win engagements that help enterprises evaluate and red team their chatbots, copilots, and AI agents - combining human expert review, LLM-as-a-judge frameworks, and adversarial red teaming.
This is a customer-facing, technical pre-sales role. You'll run discovery with client AI/ML, product, and risk teams, translate ambiguous evaluation needs into a concrete methodology and Statement of Work, and be the credible technical voice that earns the "technical win" - then hand a clean, well-scoped engagement off to delivery.
Key Responsibilities:
Pre-Sales Technical Leadership
- Partner with Account Executives throughout the sales cycle to understand a prospect's AI systems (chatbots, LLM agents, copilots) and their evaluation and risk requirements
- Lead technical discovery calls to uncover harm vectors, quality metrics, domain-specific risks, and compliance drivers relevant to the client's industry
- Design and present tailored evaluation approaches that blend human expert review, LLM-as-a-judge, and adversarial red teaming
- Build and deliver custom demos, proofs-of-concept and evaluation pilots that showcase our platform and evaluator community
- Own the technical win - unblock objections raised by client data science, ML, and AI governance stakeholders
Solution Design & Scoping
- Translate client requirements into structured Statements of Work, evaluation rubrics, test plans, and scope/pricing estimates
- Define evaluation criteria (accuracy, relevance, harmlessness, tone, hallucination rate, bias, brand alignment, etc.) tailored to the client's industry and regulatory context
- Design red-team attack strategies and threat models (prompt injection, jailbreaking, role-play, obfuscation) specific to the client's chatbot or agent use case
- Validate proposed engagements against our delivery and expert-recruiting capabilities before they're sold
Cross-Functional Collaboration & Handoff
- Work closely with Sales, Delivery (Engagement Managers/Solution Specialists), Product, and Data Science to ensure a clean transition from closed deal to active delivery
- Feed prospect and market patterns back into product/tooling roadmaps
- Support RFP responses, competitive positioning, and technical objection handling
Market & Thought Leadership
- Stay current on evaluation methodologies, red-teaming techniques, and relevant frameworks (OWASP LLM Top 10, NIST AI RMF, EU AI Act)
- Represent the company in client workshops, webinars, and industry events as a subject-matter expert in AI evaluation and red teaming
Job Requirements:
Must Have
- 6–9 years of experience, including meaningful time in a customer-facing technical role (solutions engineering, forward-deployed engineering, sales engineering, or delivery) at a company that provides AI/LLM evaluation, red-teaming, or human-data services
- Hands-on experience designing or running human-in-the-loop evaluation programs, LLM-as-a-judge frameworks, and adversarial red-teaming engagements against production chatbots or AI agents, across multiple industries
- Track record scoping technical engagements into SOWs and closing enterprise deals alongside sales/account teams
- Strong understanding of LLM failure modes (hallucination, bias, toxicity, jailbreaks) and evaluation metrics
- Familiarity with adversarial testing methodologies and frameworks like OWASP LLM Top 10 and NIST AI RMF
- Excellent presentation and communication skills; credible with both technical and executive stakeholders
- Comfortable with ambiguity and managing multiple concurrent opportunities
- Bachelor's degree in CS, Data Science, or related field, or equivalent experience
Nice to Have
- Experience with GenAI evaluation tooling (e.g., LangSmith, Arize, Galileo, Patronus, or similar)
- Experience in regulated industries (financial services, healthcare)
- Familiarity with crowd- or expert-community-based delivery models
- Working Python fluency for prompt/response analysis
Why Applause?
We’re proud to cultivate an inspiring, engaging employee culture that’s consistently reflected in high employee retention rates and satisfaction. Our talented team – known as Applause Nation – is set up for success with the latest collaboration and learning tools, opportunities for career advancement, and more.
- We have a flexible work environment with top talent from across the globe
- Collaborate with an international team of 450+ passionate, talented co-workers
- Expand your portfolio with exciting, hands-on projects providing exposure to well-known, global brands
- Learn and grow through structured onboarding, in-house knowledge sessions and access to thousands of virtual courses available on demand
- Incorporate AI and other exciting technologies into your work, to help you prioritize and boost productivity
- Experience a supportive culture that emphasizes teamwork, innovation and transparency
- Share your voice! Contribute and integrate creative and innovative ideas across roles and departments
Applause Core Values:
As a global employee community, we strive to uphold the following core values, which are critical to business success and how we measure individual and team performance. Do you share our core values?
- Be Accountable: You love to take ownership, and hold yourself and others accountable to increase empowerment and success.
- Celebrate Authenticity: You love bringing your true self to work and creating genuine and trustful relationships within a diverse environment.
- In It Together: You have a team-first mindset and love collaborating with your peers.
- Create Value for Our Customers: You love delivering meaningful business impact and being a release partner for all aspects of digital quality.
- Crush Your Goals: You always strive for excellence and constantly seek ways to be better, more effective and more efficient.
Compensation:
Beyond Applause’s award-winning culture, our employee-centric benefits and competitive compensation strategy are designed to support your physical and mental well-being, promote work-life balance, and fuel your professional growth.
The salary range for this position is $130,000 - $200,000 based on experience.
Accommodations:
Applause is a place where everyone belongs and where we believe everyone deserves the exceptional. We continue to celebrate diversity and are committed to creating an inclusive, equitable environment for our employees. If you believe you require a reasonable accommodation und.er any of the legally protected characteristics, please click here to complete an accommodation request. Please note, Applause will only review requests for applications that have been submitted. We will review your qualifications and follow up with you regarding your request if your qualifications meet our current needs.
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Salary Context
This $130K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Applause, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($165K) sits 22% below the category median. Disclosed range: $130K to $200K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Applause AI Hiring
Applause has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $200K - $200K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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