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About This Role
Job Description
Position Summary
Vertex is seeking an Agent Engineer, Agentic AI Platform to help create the next generation of AI agents that deliver practical value across internal and user\-facing experiences. This role will focus on prototyping, building, and productionizing multi\-step agent workflows that are deeply integrated with enterprise systems, business processes, and digital products.
The ideal candidate is energized by fast\-moving AI technology but grounded in strong engineering discipline. This individual will use a forward deployed engineering mindset to work closely with stakeholders, rapidly iterate on real\-world use cases, connect agents to APIs and business systems, and continuously improve quality through measurement and feedback. The Agent Engineer will play an important role in shaping how AI agents are adopted and used day to day across Vertex.
Key Responsibilities
- Design, prototype, and productionize AI agents and multi\-step agent workflows for internal and user\-facing use cases
- Apply a forward deployed engineering approach by working closely with end users, product teams, and business stakeholders to solve real workflow problems quickly and effectively
- Build agents that can reason across steps, invoke tools, call APIs, retrieve information, and take actions within defined enterprise guardrails
- Integrate agents with internal platforms, third\-party applications, enterprise APIs, and business\-critical systems
- Develop reusable agent patterns, components, prompts, tools, and orchestration logic that can be scaled across use cases
- Rapidly iterate on agent behavior based on usage data, qualitative feedback, evaluation results, and operational metrics
- Improve agent quality through testing, experimentation, benchmarking, and structured measurement of outcomes
- Partner with platform, integration, data, security, and product teams to ensure agents are secure, reliable, maintainable, and aligned with enterprise architecture
- Help define best practices for agent design, tool use, fallback behavior, escalation paths, and human\-in\-the\-loop workflows
- Support deployment, monitoring, troubleshooting, and optimization of agents in production environments
- Contribute to observability practices for agent execution, including logging, tracing, performance tracking, and issue analysis
- Ensure solutions are designed with security, compliance, and responsible AI principles in mind
- Stay current on emerging patterns, frameworks, and technologies in generative AI and agentic systems, and translate them into practical applications for Vertex
Required Qualifications
- Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related technical field; equivalent practical experience may be considered
- Experience in software engineering, AI engineering, machine learning engineering, or intelligent application development
- Hands\-on experience building applications powered by large language models, AI workflows, or agentic systems
- Experience integrating software applications with APIs, data sources, and enterprise systems
- Demonstrated experience using AI\-assisted software development and autonomous coding agents to design, generate, test, review, debug, optimize, and refactor code across complex enterprise systems.
- 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
- Demonstrated ability to move from prototype to production while balancing speed, quality, and operational rigor
- Experience working directly with users or stakeholders to iteratively refine technical solutions based on real\-world needs
- Strong problem\-solving and systems\-thinking skills
- Strong written and verbal communication skills with the ability to work effectively across technical and non\-technical teams
Technical Skills Required
- Large language model application development
- Agentic workflow design and orchestration
- Prompt engineering and prompt iteration
- Tool use and API integration for AI agents
- Multi\-step workflow automation
- Rapid prototyping and productionization
- Evaluation, testing, and quality measurement for AI systems
- Application logging, monitoring, and observability
- Distributed systems and service integration
- Secure software development practices
- Enterprise application integration
- Cloud\-based application development
- Version control, CI/CD, and deployment practices
Preferred Skills
- Experience using a forward deployed engineering or similar embedded delivery model
- Experience building internal copilots, assistants, or task\-oriented AI agents
- Familiarity with orchestration frameworks, agent runtimes, tool\-calling architectures, and retrieval patterns
- Experience integrating with enterprise collaboration tools, workflow systems, document platforms, or knowledge repositories
- Familiarity with evaluation frameworks for AI quality, groundedness, task completion, and reliability
- Experience designing human\-in\-the\-loop workflows and safe failure or escalation mechanisms
- Exposure to AI observability, tracing, and operational analytics
- Experience in regulated industries such as biotechnology, pharmaceuticals, healthcare, or life sciences
\#LI\-HYBRID
Pay Range:
$166,640 \- $250,000
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.
Flex Designation:
Hybrid\-Eligible Or On\-Site Eligible
Flex Eligibility Status:
In this Hybrid\-Eligible role, you can choose to be designated as:
1\. Hybrid : work remotely up to two days per week; or select
2\. On\-Site : work five days per week on\-site with ad hoc flexibility.
Note: The Flex status for this position is subject to Vertex’s Policy on Flex @ Vertex Program and may be changed at any time.
\#LI\-Hybrid
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 $166K-$250K range is above the median 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. Disclosed range: $166K to $250K.
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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