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About This Role
Scientific Strategy, Key Innovation Leaders & Emerging OpportunitiesHarrison Hayes | New York / Hybrid / Remote
Harrison Hayes is seeking an unusually curious AI Life Sciences Intelligence Extern/Intern to help us identify difficult scientific and strategic problems across biotechnology, pharmaceuticals and medical technology.
This is not a conventional business development internship.
Your job is to find the questions companies don't know how to answer.
A Phase II trial fails, but a subgroup responds. A promising molecule demonstrates biological activity but insufficient differentiation. A company has six potential indications and capital for two. A regulatory agency questions whether an observed benefit can actually be attributed to the therapy. A clinically interesting asset is abandoned because it no longer competes for corporate capital.
These are the situations we want you to find.What You'll DoUsing ChatGPT, Claude, Perplexity, scientific databases and primary-source research, you will:
- Identify emerging scientific, clinical and strategic problems across life sciences
- Analyze failed or ambiguous clinical trials, pipeline changes, regulatory decisions and portfolio restructurings
- Find situations where the obvious interpretation may be incomplete
- Determine the unanswered question behind the business problem
- Identify the scientists, clinicians, regulatory experts, payers and other specialists required to answer it
- Develop concise opportunity briefs for Harrison Hayes senior leadership
- Help identify potential advisory, licensing, M&A, financing and investment opportunities arising from those insights
Why Harrison Hayes
Large consulting organizations are designed to solve broad strategic and organizational problems. Harrison Hayes is interested in something narrower and more specialized:
What happens when the problem is too scientifically specific for a generalized answer?
Through our Key Innovation Leader Networks, we assemble purpose-built groups of leading clinicians, scientists, translational researchers, regulatory specialists, payers, reimbursement experts, medical economists and former pharmaceutical executives around a single decision.
The objective is not to validate management's strategy.
It is to challenge it.
Who We're Looking For
You might be studying biotechnology, medicine, pharmacology, biomedical engineering, finance, business or data science.
More important is how you think.
You should be comfortable moving from a scientific paper to an SEC filing, from a clinical endpoint to an earnings call, and from an apparently failed drug to the question:
“Did the molecule fail, or did the experiment fail to answer the right question?”
We want someone who uses AI aggressively but skeptically. AI is your research engine.
Judgment is the job.
To ApplySend us your résumé and a brief note explaining how you use AI for serious research.
Better yet, find us a problem.Identify one life-sciences company facing a difficult scientific or clinical decision. Tell us what happened, why the obvious interpretation may be incomplete, what management should actually be asking, and which Key Innovation Leaders you would assemble to answer it.
We are not looking for the person who finds the most information. We are looking for the person who finds the question everyone else missed.
Summary
Join our innovative team as a Life Sciences AI Intelligence Extern / Intern, where you will immerse yourself in cutting-edge artificial intelligence and machine learning projects tailored to the life sciences sector. This paid internship offers a unique opportunity to develop your skills in data analytics, statistical modeling, and AI implementation while contributing to impactful research and development initiatives. You will collaborate with experienced data scientists, bioinformaticians, and engineers to explore big data systems, natural language processing, and predictive modeling analysis that drive breakthroughs in healthcare and biological research.
Duties
- Assist in designing and implementing AI models using frameworks such as TensorFlow and machine learning cloud services to analyze complex biological datasets.
- Support the development of scalable big data systems utilizing Hadoop, Spark, and ETL processes for efficient data ingestion and processing.
- Contribute to statistical analysis for research projects employing tools like R, SAS, and statistical analysis software to derive meaningful insights.
- Collaborate on model training, evaluation, and deployment of AI models focused on predictive modeling analysis and generative AI applications.
- Help optimize database design and SQL database management to facilitate seamless data mining and data analytics workflows.
- Participate in natural language processing tasks using machine learning frameworks to analyze unstructured biomedical text data.
- Engage in cross-disciplinary teamwork to support AI implementation efforts across various life sciences research initiatives.
Experience
- Currently pursuing or recently completed a degree in Computer Science, Data Science, Bioinformatics, Statistics, or related fields.
- Familiarity with programming languages such as Python, Java, C, or Bash (Unix shell) for data manipulation and model development.
- Hands-on experience with machine learning frameworks like TensorFlow or similar tools is highly desirable.
- Knowledge of cloud services such as AWS or other machine learning cloud platforms is a plus.
- Understanding of big data technologies including Hadoop, Spark, Talend, and Looker for data processing and analytics.
- Exposure to statistical analysis tools like R or SAS for research purposes is preferred.
- Strong analytical skills with an interest in applying AI solutions within the life sciences domain are essential.
Embark on this exciting journey where your passion for AI meets the transformative world of life sciences! This paid externship is designed to empower your growth while making meaningful contributions to scientific advancements that improve health outcomes worldwide.
Work Location: Remote
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Harrison Hayes, LLC, 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 $212,000 based on 11,857 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Harrison Hayes, LLC AI Hiring
Harrison Hayes, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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