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About This Role
Department:
101180 Digital \& Enterprise Technology
Location:
San DiegoBe a part of a global team that is inspired to make a difference in the lives of people living with rare disease.
At Travere Therapeutics, we recognize that our exceptional employees are vital to our success. We are a dedicated team focused on meeting the unique needs of rare patients. Our work is rewarding – both professionally and personally – because we are making a difference. We are passionate about what we do.
We are seeking talented individuals who will thrive in our collaborative, diverse, fast\-paced environment and share in our mission – to identify, develop and deliver life\-changing therapies to people living with rare disease. We stick by our values centered on patients, courage, community, and collaboration to pursue our vision of becoming a leading biopharmaceutical company dedicated to the delivery of innovation and hope to patients in the global rare disease community.
At Travere Therapeutics, we are in rare for life. We continue to courageously forge new paths as we move toward a common goal of elevating science and service for rare patients*.*
Position Summary:
Travere is seeking an entrepreneurial and strategic leader to help drive our AI agenda and strategy. As the Sr. Director, AI Strategy \& Engineering, you will lead the development and execution of the enterprise AI strategy, enabling the organization to responsibly and effectively leverage AI across all business functions. This leader will identify high\-value, high\-impact AI opportunities, define an enterprise AI roadmap that is aligned with the broader Digital and Enterprise Technology vision, and drive adoption of generative AI, machine learning, and intelligent automation capabilities that accelerate innovation, improve productivity, and create measurable business value.As a member of the Digital Innovation \& Enterprise Technology Leadership Team, this role will serve as the organization's strategic AI subject matter expert while also developing and leading the AI engineering function for Travere. The position requires a unique blend of strategic vision, technical, engineering acumen, and organizational leadership.
Responsibilities:
Enterprise AI Strategy
- Develop and own the enterprise AI strategy, vision, and multi\-year roadmap aligned with corporate objectives and broader Digital Innovation and Enterprise Technology transformation priorities.
- Identify, prioritize, and validate AI use cases across all business functions, including Commercial, Medical Affairs, R\&D, Finance, and People Success.
- Establish a repeatable framework for evaluating AI opportunities based on business impact, technical feasibility, risk, and organizational readiness.
- Continuously assess the rapidly evolving AI landscape and recommend emerging capabilities that provide strategic advantage.
AI Engineering \& Delivery
- Lead the Digital Innovation team’s AI engineering capabilities and execution of enterprise AI initiatives.
- Partner closely with Enterprise Technology \+ Solutions to define the target AI architecture, platforms, integration patterns, security standards, and operational model.
- Oversee development and deployment of generative AI applications, intelligent assistants, AI agents, knowledge retrieval solutions, and workflow automation capabilities.
- Ensure AI solutions are scalable, secure, governed, and integrated into the enterprise technology ecosystem.
Generative AI Leadership
- Define the organization's generative AI strategy, including platform selection, model usage, enterprise tools/copilots, retrieval\-augmented generation, agentic AI, and emerging foundation model capabilities.
- Develop standards for responsible and effective use of generative AI across the enterprise.
- Drive adoption of enterprise AI capabilities by identifying reusable platforms and shared services that maximize organizational value.
- Evaluate build\-versus\-buy decisions for AI platforms and capabilities.
Vendor Strategy \& Assessments
- Evaluate third\-party AI vendors, products, and strategic partnerships.
- Lead technical and business assessments of AI platforms, startups, and enterprise software capabilities.
- Negotiate technical direction with partners to ensure solutions meet enterprise standards and long\-term architectural goals.
- Maintain awareness of market trends, competitive technologies, and regulatory developments impacting enterprise AI.
Governance \& Responsible AI Deployment
- Partner with Cybersecurity, Legal, Compliance and Enterprise Technology \+ Solutions on AI governance, policies, and responsible AI practices.
- Ensure AI initiatives comply with applicable regulations, data governance standards, cybersecurity requirements, and ethical AI principles.
- Define metrics to measure AI adoption, business outcomes, value realization, and operational effectiveness.
Cross\-Functional Leadership
- Serve as a trusted advisor to business functions and leadership on AI strategy, investment priorities, and organizational capabilities.
- Build strong partnerships across Enterprise Technology \+ Solutions, Digital Innovation, and business functions.
- Lead cross\-functional teams and committees responsible for delivering enterprise AI capabilities.
- Foster an innovation culture that encourages experimentation while maintaining appropriate governance and risk management.
- Develop enterprise AI literacy and adoption programs.
- Coach leaders and teams on identifying opportunities to leverage AI effectively.
- Establish best practices, reusable frameworks, and centers of excellence that accelerate AI delivery across the organization.
- Build and develop a high\-performing AI engineering and strategy team via FTEs and MSPs.
Education/Experience Requirements:
- Bachelor’s degree required. Advanced degree in Computer Science, Engineering, Information Systems, Data Science, or related field preferred. Equivalent combination of education and applicable job experience may be considered.
- 12\+ years of progressive experience in technology leadership, software engineering, data platforms, AI/ML, or digital transformation in life sciences/pharmaceuticals/biotech.
- 5\+ years leading enterprise AI, machine learning, or advanced analytics initiatives.
Additional Skills/Experience/Requirements:
- Strong understanding of modern AI technologies, including:
- Large Language Models
- Generative AI
- AI agents and agentic workflows
- Retrieval\-Augmented Generation
- Machine Learning
- Prompt engineering
- Vector databases
- Enterprise AI platforms
- Demonstrated success developing enterprise AI strategies and pull through into measurable business impact.
- Experience leading cross\-functional technology initiatives across multiple business domains.
- Experience evaluating enterprise technology vendors and managing strategic technology partnerships.
- Strong executive communication and stakeholder management skills.
- Vision \& Execution: Ability to set strategic direction while rolling up sleeves to build early\-stage capabilities.
- Communication: Exceptional communicator, able to translate complex topics into business\-relevant insights
- Strong and strategic leader with ability to work cross functionally with ease.
- Previous line management experience with the ability to effectively manage performance, engage team members, provide coaching, and respond to situations affecting staff.
- Exercises excellent judgement in escalating issues and problems where necessary and determining appropriate project communications to internal and external stakeholders.
- Demonstrates an empathic approach to problem\-solving, with a willingness to work across all levels of leadership and functional areas in a growing, changing, and fast\-moving environment.
- Demonstrates an ability to influence without authority in a grounded manner.
- Ability to travel up to 10% domestically and internationally.
- All positions have an essential job function to be able to perform face to face work with colleagues and/or onsite in San Diego. No role is expected to be 100% remote.
Total Rewards Offerings:
*Travere provides comprehensive total rewards offerings that demonstrate our commitment as a diverse, equitable, people\-centric, and pay\-for\-performance* *organization.*
*Benefits:* *Our benefits include premium health, financial, work\-life and well\-being offerings for eligible employees and dependents, wellness and employee support programs, life insurance, disability, retirement plans with employer match and generous paid time off.*
*Compensation*: *Our competitive compensation package includes a combination of both cash compensation (base pay and short\-term incentive) and long\-term incentive compensation (company stock), designed to recognize, retain, and reward employees.*
Target Base Pay Range:
$219,000\.00 \- $285,000\.00* *This information is current as of the date of this posting and may be modified in the future. Actual pay offered to a candidate will depend on a variety of factors including the candidate’s experience, education, skills, and location.*
Travere will accept applications on an ongoing basis until a candidate is selected for the position.
Travere Therapeutics, Inc. is an EEO/AA/Veteran/Disability Employer.
If you require a reasonable accommodation to complete the application or interview process, please contact us by sending an email to accommodations@travere.com. Please note that this email address is to be used exclusively to request an accommodation with the online application, interview or hiring process only. Travere HR will not reply to emails sent to this address for any other reason.
Salary Context
This $219K-$285K range is above the 75th percentile 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 Travere Therapeutics, 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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($252K) sits 15% above the category median. Disclosed range: $219K to $285K.
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.
Travere Therapeutics AI Hiring
Travere Therapeutics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $285K - $285K.
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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