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About This Role
Job Description: Position Summary
We are seeking a consultant to partner with Biopharmaceutical Sciences and Manufacturing teams to develop and implement an enterprise AI/ML strategy that accelerates biologics and cell therapy CMC development. The role will deliver near-term productivity improvements through AI tools, copilots, agents, and employee enablement; develop a longer-term roadmap for AI-enabled laboratory and manufacturing transformation; and support execution of an automated laboratory pilot.
This is a 6 month contract to start.
Onsite at Vertex Pharmaceuticals.
W2 position.
Key Responsibilities
- Develop a strategic roadmap for AI/ML deployment across Biopharmaceutical Sciences and Manufacturing functions.
- Identify high-value use cases for AI, machine learning, automation, and advanced analytics within Process Development, Analytical Development, Manufacturing Sciences, and technical operations.
- Develop and deploy high-impact micro-productivity AI tools, copilots, and agents for scientific and operational workflows, including scientific knowledge retrieval, report and protocol drafting, literature synthesis, data interpretation, technical documentation, regulatory-response support, and project management.
- Develop and deploy AI-enabled laboratory automation, data-driven experimentation, and closed-loop development platforms that connect automated equipment, analytical systems, and advanced AI/ML models.
- Develop a roadmap for macro-productivity improvement through AI-enabled manufacturing technologies, including advanced process monitoring, multivariate analytics, soft sensors, predictive modeling, anomaly detection, digital twins, advanced process control, and manufacturing workflow optimization
- Support pilot projects and establish a scalable framework for broader enterprise deployment.
- Align data, technology, governance, validation, human oversight, and change-management requirements for responsible deployment in regulated environments. Required Qualifications
- MS or PhD in Biotechnology, Engineering, Data Science, Computer Science, or related discipline.
- 5+ years of experience in biopharmaceutical development, laboratory automation, AI/ML, or related fields.
- Strong knowledge of biologics and/or cell therapy CMC development.
- Experience with laboratory automation, robotics, ELN/LIMS, data integration, and scientific data analytics.
- Demonstrated experience applying AI/ML to scientific, engineering, or manufacturing challenges.
- Understanding of Agentic AI frameworks, workflow orchestration, autonomous agents, and human-in-the-loop governance models.
- Ability to bridge scientific, technical, and business stakeholders to drive adoption and implementation of AI-enabled solutions.
- Strong understanding of data governance principles, data architecture, ontology, data quality management, and enterprise AI governance.
- Familiarity with regulatory expectations and industry best practices related to AI deployment in GxP, GMP, pharmaceutical, biotechnology regulated environments.
Pay Range:
$100-$110/HR Requisition Disclaimer:
This job posting is for a temporary role with a third-party agency partner on assignment at Vertex Pharmaceuticals (“Vertex”). The individual selected for this role will be offered the role as an employee of a third- party agency partner; compensation, medical benefits, fringe benefits and other terms and conditions of employment shall be presented by the third-party agency partner upon offer. The pay rate range provided is a reasonable estimate of the anticipated compensation range for this job at the time of posting. The actual pay rate will be based on a number of factors, including skills, competencies, experience, location and/or being pursued and other job-related factors permitted by law. In addition, this role will be eligible for overtime pay, in accordance with federal and state requirements
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No C2C or Third-Party Vendors
Salary Context
This $208K-$228K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Vertex Pharmaceuticals, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $208K to $228K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Vertex Pharmaceuticals AI Hiring
Vertex Pharmaceuticals has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Boston, MA, Boston, MA, US. Compensation range: $228K - $228K.
Location Context
AI roles in Boston pay a median of $215,000 across 323 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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