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About This Role
Company Description
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Hirevue is where hiring happens – transforming the way organizations discover, engage, and hire the best talent. Connecting companies and candidates anytime, anywhere, Hirevue’s end\-to\-end hiring platform features video interviewing, assessments and conversational AI. The industry leader in science backed, modern hiring solutions powered by ethical AI, Hirevue has hosted more than 70 million video interviews and 200 million chat\-based candidate engagements for over 1200 pioneering customers around the globe.
Job Description
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As a Data Science Intern, you will support ongoing research initiatives that keep our enterprise products and our clients' hiring processes on the absolute cutting edge and leverage machine learning to shape the future of hiring.
Partnering closely with our world\-class data scientists and cross\-functional partners in Product Science and Product and Technology, you will have the opportunity to build, validate, and scale machine learning models and agentic workflows. You will also conduct applied research on systems and processes to continuously advance our natural language processing (NLP) and ethical AI capabilities.
Essential Duties and Responsibilities
Applied Research \& Modeling
- Support ongoing research initiatives that keep us on the cutting edge, including in agentic workflow development
- Work with machine learning models to build products and conduct research
- Write code to develop prototypes and utilities to extend or improve our product suite
Data Preparation \& Quality
- Drive the preparation of data used to train deep learning models and score unstructured data
- Assure quality of our software in terms of content, scoring, and functionality
- Performance modeling and data analysis
Qualifications
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Required
- Bachelor’s degree required, with a Master’s degree or PhD in progress in a quantitative/programming field (Computer Science, Statistics, Mathematics, Engineering, Psychology, Economics, Data Science, or related)
- Understanding of statistics, machine learning, and deep learning fundamentals
- Proficiency in Python, including common ML libraries (e.g., numpy, pandas, scikit\-learn)
- Experience with version control (e.g., Git)
- Ability to analyze large datasets and draw meaningful, actionable insights
- Strong problem\-solving skills — able to synthesize information from multiple sources to solve problems
- Team player with strong collaboration and communication skills; comfortable working closely with data scientists, engineers, and I/O Psychologists
- Self\-motivated and able to work effectively with limited supervision; strong prioritization skills
Preferred
- Awareness of state of the art methods in machine learning, deep learning, NLP, and/or generative AI
- Exposure to large language models (LLMs) and generative AI (e.g., prompt engineering, fine\-tuning, agentic design patterns, agentic evaluation methods)
- Familiarity with SQL and database querying
- Familiarity with cloud platforms (AWS preferred) for ML workflows
Additional Information
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All team members at Hirevue are expected to embody our core values, which are the H.E.A.R.T. of Hirevue. They are: Hero for our Customers, Enjoy the Journey, Always do the Right Thing, Reach Forwards, and Take Action and Go.
Hirevue takes security seriously and has measures in place to protect work\-related data in a remote setting. Our organization has implemented a BYOD (Bring Your Own Device) policy, HireVue uses Google User Enrollment, which ensures personal apps and data are kept separate from work apps and data should you choose to use your personal device for work purposes. HireVue can manage only the work\-related aspects of the device, ensuring privacy for personal data.
*Hirevue is committed to equal treatment and opportunity in all aspects of recruitment, selection, and employment. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other category protected under the law. HireVue is an equal opportunity employer; committed to a community of inclusion, and an environment free from discrimination, harassment, and retaliation. All your information will be kept confidential according to EEO guidelines.*
*Hirevue is NOT currently hiring in:*
*AK, HI, IA, ME, MS, NM or WV.*
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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 HireVue, Inc., 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. Entry-level AI roles across all categories have a median of $120,000.
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.
HireVue, Inc. AI Hiring
HireVue, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Sandy, UT, US.
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
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