Interested in this AI/ML Engineer role at IDC Research Inc.?
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Overview:
About the Role \& Team
IDC is seeking a Research Vice President to join our established worldwide artificial intelligence, data and automation (AD\&A) research domain to focus on using our research on the modeling of AI business value and cost to advise clients. The candidate must have demonstrable experience in devising and implementing transformation strategies. Experience with AI platforms is preferred. The candidate will also collaborate with other analysts covering adjacent technologies, including other forms of AI and Automation technologies. Research will include understanding functional and industry use cases, as well as the key determinants of AI value realization.
What You’ll Do
- Use the IDC AI Economic Model to assist clients in selecting, prioritizing, and forecasting the value of AI use case portfolios.
- Collaborate with the analyst community to build the model and to help them use the models in their own research.
- Work with AI leadership team and engineering to create an AI Economics interactive tool that are clients can use to manage their initiatives.
- Partner with our sourcing advisory team to incorporate ongoing price benchmarking into the models.
- Contribute to the production of both syndicated and custom research.
What You Bring* Five to ten years experience required.
- Bachelor’s degree is required; Master’s degree preferred.
- Practitioner experience working in a financial analysis and/or business transformation capacity implementing AI projects.
- First\-hand experience building economic impact and/or financial models related to technology investment.
- Strong knowledge of the overall AI technology sector is preferred, along with the ability to analyze trends and provide actionable guidance to technology providers and buyers. Direct experience in this area is ideal.
- Up\-to\-date knowledge of innovations in large language models and the AI industry more broadly.
- Knowledge of AI deployment models and the advantages and disadvantages of each.
- Demonstrated ability to analyze survey data and normalize findings to support the building of analytic models.
- Excellent written and verbal communication skills, with the ability to analyze, present, and articulate complex data sets.
- Strong interpersonal and organizational skills, with the capacity to work both independently and within a team.
- Proven ability to read and evaluate financial reports to analyze key drivers that impact companies’ ability to differentiate and remain competitive.
- Ability to travel 25\-30% for client meetings, industry events, and customer projects.
- This role offers the flexibility to work remotely from an approved U.S. location, or onsite at our corporate office in Boston, MA.
Why This Role Stands Out
At IDC, your work helps shape how the world understands technology and where it goes next. You collaborate with curious, high\-caliber colleagues who value rigor, integrity, and shared success. As the premier global provider of trusted technology intelligence, IDC equips business and technology leaders with the evidence they need to make confident decisions. Our insights inform strategy, investment, and innovation across industries and regions.
Recognized by IIAR as Analyst Firm of the Year for five consecutive years, IDC sets the standard for credibility and impact. With more than 1,000 analysts worldwide and a truly global perspective, we combine deep expertise with practical relevance. Here, your ideas matter, your voice is heard, and your contributions provide the insights leaders rely on every day. It is meaningful work, backed by a culture that supports growth, collaboration, and long\-term career development with a globally respected brand. What We Offer* 15 vacation days (prorated based on start date)
- 12 company\-paid holidays
- 6 paid sick days (prorated based on start date; may vary by state)
- Medical, dental, and vision coverage
- 2 floating holidays (prorated based on start date)
- 1 volunteer day
- 401(k) company match (IDC matches 3% on the first 6% of employee contributions)
- Company\-paid short\-term disability
- Company\-paid life insurance
- Company\-paid parental leave
Compensation Transparency
At IDC, we are committed to fair and equitable pay practices. Employees are compensated equitably for their work, aligned with their skills and experience. Salary and incentive structures are determined through a rigorous process that considers experience, education, certifications, role\-specific requirements, internal equity, and verified U.S. market data from an independent third\-party partner.
The expected total annual compensation, depending on location and experience, is between $180,000 – $240,000, inclusive of base salary and variable compensation. Equal Opportunity Employer*IDC is committed to providing equal employment opportunities for all qualified persons. Employment eligibility verification required. We participate in E\-Verify.*
IDC is currently able to employ remote workers in the following states: Arizona (AZ), California (CA), Colorado (CO), Connecticut (CT), Washington D.C. (DC), Florida (FL), Georgia (GA), Illinois (IL), Indiana (IN), Kansas (KS), Massachusetts (MA), Maryland (MD), Maine (ME), Michigan (MI), Minnesota (MN), Missouri (MO), Mississippi (MS), North Carolina (NC), New Hampshire (NH), New Jersey (NJ), New York (NY), Ohio (OH), Oregon (OR), Pennsylvania (PA), Rhode Island (RI), South Carolina (SC), Tennessee (TN), Texas (TX), Utah (UT), Virginia (VA), Vermont (VT), Washington (WA), and Wisconsin (WI).
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\#LI\-Remote
Salary Context
This $180K-$240K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At IDC Research 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 in Demand for This Role
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. Disclosed range: $180K to $240K.
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.
IDC Research Inc. AI Hiring
IDC Research Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $240K - $240K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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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