Interested in this AI/ML Engineer role at Vertex Pharmaceuticals?
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About This Role
Job Description
Position Summary
Vertex is seeking a Sr. Principal AI Engineer, Agentic AI Platform to design, build, and optimize the shared platform capabilities that power AI\-enabled products and intelligent workflows across the enterprise. This role will focus on delivering production\-grade platform services for model integration, prompt and workflow orchestration, evaluation, observability, performance optimization, and agent lifecycle management.
A key focus of this role will be enabling a build/bring\-your\-own\-agents capability within the Agentic AI Platform, allowing teams across Vertex to create, integrate, customize, and operationalize their own agents using shared platform standards, tooling, and governance controls.
The ideal candidate combines strong software engineering fundamentals with deep experience in applied AI systems. This individual will be comfortable operating across rapid experimentation and engineering rigor, translating emerging AI capabilities into scalable, reliable, secure, and reusable platform components. The Senior Principal AI Engineer will play a critical leadership role in accelerating AI adoption across Vertex by enabling product teams to build and deploy AI solutions faster and more effectively.
Key Responsibilities
- Architect and develop shared AI/agentic platform services that support enterprise AI products and internal workflows
- Design and implement a build/bring\-your\-own\-agents capability that enables teams to create, register, integrate, deploy, and manage their own agents within the enterprise agentic platform
- Establish reusable frameworks, SDKs, templates, interfaces, and guardrails that standardize how custom agents are built and onboarded onto the platform
- Define agent lifecycle capabilities including agent registration, configuration, testing, deployment, monitoring, versioning, and retirement
- Build and maintain robust integrations with foundation models, model gateways, APIs, enterprise tools, and related AI infrastructure
- Design and implement systems for prompt orchestration, workflow execution, tool use, memory patterns, and agentic task coordination
- Develop reusable frameworks and services for evaluation, benchmarking, and validation of AI model, agent, and workflow performance
- Establish platform capabilities for observability, monitoring, tracing, logging, and alerting across AI workloads and autonomous agent interactions
- Optimize platform performance, scalability, latency, reliability, and cost efficiency for production AI and agentic systems
- Partner with product, data, engineering, security, and architecture teams to enable enterprise\-ready AI solutions
- Translate prototypes and experimental concepts into hardened, maintainable, production\-grade services
- Define engineering standards, best practices, and design patterns for AI platform development and deployment
- Support governance, risk management, and responsible AI practices through measurable controls, policy enforcement, and technical safeguards for agent behavior
- Drive platform adoption by creating reusable components, documentation, onboarding patterns, and developer enablement resources
- Mentor engineers and provide technical leadership across AI platform initiatives
- Evaluate emerging tools, frameworks, and architectural patterns in generative AI and agentic systems to inform platform strategy
Required Qualifications
- Bachelor’s degree in Computer Science, Software Engineering, Machine Learning, Data Engineering, or a related technical field; advanced degree preferred
- Significant industry experience in software engineering, machine learning engineering, or AI platform development, including experience in senior or principal\-level technical roles
- Proven track record designing and delivering production\-scale AI or ML platforms
- Strong experience building distributed systems, APIs, microservices, and cloud\-native applications
- Demonstrated experience operationalizing machine learning, generative AI, or agent\-based solutions in enterprise environments
- Experience designing extensible platform capabilities that enable internal teams to build or integrate custom applications, tools, or services
- Deep understanding of software engineering best practices including testing, CI/CD, version control, code review, and system reliability
- Deep understanding of AI\-native software engineering practices and experience establishing standards, governance, and best practices for the responsible use of AI coding assistants and software engineering agents across engineering teams
- Experience defining architecture, standards, and reusable services for large\-scale enterprise environments
- Experience leading complex technical initiatives and influencing architecture across cross\-functional teams
- Strong communication skills with the ability to explain complex technical concepts to varied audiences
- Experience balancing experimentation speed with production engineering discipline, security, and maintainability
Technical Skills Required
- AI/ML platform architecture
- Generative AI systems and large language model integration
- Agentic workflows and orchestration frameworks
- Multi\-agent or autonomous agent system design
- Prompt engineering and prompt management
- Workflow orchestration and automation
- Agent lifecycle management
- Model evaluation, benchmarking, and performance measurement
- AI observability, tracing, monitoring, and logging
- API design and service integration
- Distributed systems and scalable backend engineering
- Cloud platforms and cloud\-native deployment patterns
- Productionization of AI/ML services
- Reliability, latency, throughput, and cost optimization
- CI/CD pipelines and DevOps/MLOps practices
- Secure software development and enterprise platform controls
Preferred Skills
- Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, or a related discipline
- Experience with enterprise AI platforms, developer platforms, or internal tooling ecosystems
- Experience building frameworks or platforms that support bring\-your\-own\-component or extensible developer patterns
- Familiarity with model gateways, retrieval\-augmented generation, and evaluation frameworks
- Experience implementing AI governance, responsible AI controls, and compliance\-oriented technical solutions
- Knowledge of vector databases, knowledge retrieval systems, and orchestration layers for intelligent applications
- Experience in regulated industries such as biotechnology, pharmaceuticals, healthcare, or life sciences
- Strong mentoring and technical leadership experience in highly collaborative environments
- Ability to assess emerging AI technologies and translate them into practical platform capabilities
\#LI\-HYBRID
Pay Range:
$188,000 \- $282,000
Disclosure Statement:
The range provided is based on what we believe is a reasonable estimate for the base salary pay range for this job at the time of posting. This role is eligible for an annual bonus and annual equity awards. Some roles may also be eligible for overtime pay, in accordance with federal and state requirements. Actual base salary pay will be based on a number of factors, including skills, competencies, experience, and other job\-related factors permitted by law.
At Vertex, our Total Rewards offerings also include inclusive market\-leading benefits to meet our employees wherever they are in their career, financial, family and wellbeing journey while providing flexibility and resources to support their growth and aspirations. From medical, dental and vision benefits to generous paid time off (including a week\-long company shutdown in the Summer and the Winter), educational assistance programs including student loan repayment, a generous commuting subsidy, matching charitable donations, 401(k) and so much more.
Company Information
Vertex is a global biotechnology company that invests in scientific innovation.
Vertex is committed to equal employment opportunity and non\-discrimination for all employees and qualified applicants without regard to a person's race, color, sex, gender identity or expression, age, religion, national origin, ancestry, ethnicity, disability, veteran status, genetic information, sexual orientation, marital status, or any characteristic protected under applicable law. Vertex is an E\-Verify Employer in the United States. Vertex will make reasonable accommodations for qualified individuals with known disabilities, in accordance with applicable law.
Any applicant requiring an accommodation in connection with the hiring process and/or to perform the essential functions of the position for which the applicant has applied should make a request to the recruiter or hiring manager, or contact Talent Acquisition at [email protected]
Salary Context
This $188K-$282K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($235K) sits 9% above the category median. Disclosed range: $188K to $282K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Vertex Pharmaceuticals AI Hiring
Vertex Pharmaceuticals has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $250K - $282K.
Location Context
AI roles in Boston pay a median of $210,000 across 166 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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