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About This Role
- Job Type: Officer of Administration
- Regular/Temporary: Regular
- Hours Per Week: 35
- Standard Work Schedule:
- Building: Studebaker
- Salary Range: $120,000 \- $140,000
*The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University's good faith and reasonable estimate of the range of possible compensation at the time of posting.*
Position Summary
Reporting to the Sr. Director of AI \& Emerging Technologies, the AI Solutions Engineer will serve as a technical lead for designing and implementing AI\-enabled solutions that address business and operational needs across Columbia University. Working closely with stakeholders, the AI Generalist, and CUIT technical partners, this role will translate requirements into scalable technical solutions using programming, machine learning, large language models, prompt engineering, data engineering, automation technologies, APIs, and enterprise AI platforms. The AI Solutions Engineer will support proof\-of\-concept development, architecture and tool selection, optimization of AI capabilities, and the responsible deployment of solutions aligned with University standards.
The Emerging Technologies team is a fast\-paced, startup inspired group that develops extremely innovative solutions to some of the most challenging problems in higher education and research.
The ideal candidate will have the following skillset:
- Technical AI Solutions Lead \- You can design practical AI\-enabled solutions that balance user needs, technical feasibility, security, scalability, and supportability.
- Hands\-On Builder \- You are comfortable developing prototypes, integrations, automations, data workflows, prompts, and proof\-of\-concepts using modern AI and software tools.
- Consultative Engineer \- You can partner with non\-technical stakeholders to clarify requirements and explain tradeoffs in accessible language
- Enterprise\-Minded Architect \- You understand that successful AI solutions require governance, privacy, accessibility, reliability, documentation, and operational handoff.
- Continuous Learner \- You stay current with rapidly evolving AI platforms, model capabilities, development patterns, and responsible AI practices.
The successful candidate will be a pragmatic, hands\-on engineer who can move from ambiguity to working solutions while partnering effectively across CUIT and ensuring AI capabilities are implemented responsibly, securely, and at enterprise scale.
Responsibilities
- AI Solution Design: Translates business and operational requirements into technical designs, solution options, implementation plans, and recommendations for AI\-enabled services.
- Proof\-of\-Concept Development: Build and evaluate prototypes, pilots, automations, integrations, and proof\-of\-concepts using large language models, APIs, enterprise AI platforms, and related technologies.
- Prompt Engineering \& Model Optimization: Designs, tests and refines prompts, workflows, retrieval patterns and model configurations to improve solution quality, usability, and reliability.
- Data \& Integration Engineering: Develops and/or coordinates data flows, API integrations, connectors, and automation patterns required to support AI\-enabled use cases.
- Architecture \& Tool Selection: Advises on platform capabilities, vendor tools, build\-versus\-buy considerations, technical constraints, and scalable implementation approaches.
- Responsible AI \& Governance Alignment: Partners with Security/Risk, Enterprise Architecture, data owners, accessibility partners, and other stakeholders to ensure solutions align with University policies and standards.
- Technical Documentation \& Handoff: Creates technical documentation, implementation notes, support handoff materials, and reusable patterns to enable operational support and future reuse.
- Stakeholder Collaboration: Works closely with the AI Generalist, Emerging Technologies team members, faculty, staff, business units, vendors, and CUIT partners to deliver high\-value solutions.
- Continuous Improvement: Identifies reusable components, accelerators, automation patterns, evaluation methods, and technical standards that improve the maturity of AIaaS delivery.
- All other duties as assigned.
Minimum Qualifications
- Bachelor's degree and/or its equivalent required.
- Minimum 4\-6 years’ related experience.
- 4\-6 years of progressively responsible experience in software engineering, solutions engineering, systems integration, data engineering, automation, machine learning, or related technical roles.
- Hands\-on experience with programming or scripting languages such as Python, JavaScript, TypeScript, PHP, or similar languages.
- Experience building solutions with APIs, automation platforms, data pipelines, integrations, or cloud/enterprise platforms.
- Working knowledge of large language models, prompt engineering, AI application patterns, machine learning concepts, and responsible AI considerations.
- Demonstrated ability to translate business requirements into technical designs, prototypes, and production\-ready or supportable solution approaches.
- Strong analytical, troubleshooting, documentation, and communication skills, including the ability to explain technical tradeoffs to non\-technical stakeholders.
- Ability to manage multiple concurrent initiatives in a fast\-paced, deadline\-driven environment with changing priorities.
- Ability to work with minimal supervision and exercise sound judgment when handling sensitive, ambiguous, or high\-visibility requests.
- Demonstrated ability to work in a fast\-paced, deadline driven environment.
- Demonstrated excellence in a variety of competencies including teamwork/collaboration, analytical thinking, communication and influencing skills, and technical expertise.
- Ability to work with changing priorities and with multiple projects.
- Ability to be precise and attentive to detail is essential.
- Ability to work with minimal supervision and exercise sound judgment when handling sensitive, ambiguous, or high\-visibility requests.
- Ability to work occasional evening/off\-hour work as needed to support major launches, pilots, or critical service milestones.
Preferred Qualifications
- Experience in higher education, research, healthcare, or similarly complex regulated environments.
- Experience with enterprise AI platforms, LLM application frameworks, retrieval\-augmented generation, vector databases, workflow automation, or agentic AI patterns.
- Experience with cloud platforms, DevOps practices, secure software development, source control, testing, monitoring, or CI/CD practices.
- Experience integrating AI capabilities into enterprise applications, websites, collaboration platforms, or business process workflows.
- Familiarity with accessibility, privacy, security, data governance, and risk considerations for AI\-enabled solutions.
- Experience supporting vendor evaluations, technical pilots, and implementation planning for enterprise platforms and services.
Equal Opportunity Employer / Disability / Veteran
Columbia University is committed to the hiring of qualified local residents.
Salary Context
This $120K-$140K range is in the lower quartile 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 Columbia University, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($130K) sits 41% below the category median. Disclosed range: $120K to $140K.
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.
Columbia University AI Hiring
Columbia University has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Manhattanville, NY, US. Compensation range: $115K - $140K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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