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About This Role
### Who we are:
Kymera is a clinical\-stage biotechnology company pioneering the field of targeted protein degradation (TPD) to develop medicines that address critical health problems and have the potential to dramatically improve patients' lives. Kymera is deploying TPD to address disease targets and pathways inaccessible with conventional therapeutics. Having advanced the first degrader into the clinic for immunological diseases, Kymera is focused on building an industry\-leading pipeline of oral small molecule degraders to provide a new generation of convenient, highly effective therapies for patients with these conditions. Founded in 2016, Kymera has been recognized as one of Boston's top workplaces for the past several years. For more information about our science, pipeline and people, please visit www.kymeratx.com or follow us on X (formerly Twitter) or LinkedIn.
### How we work:
- PIONEER: We are courageous, resilient and rigorous in our mission to improve patients' lives through our revolutionary degrader medicines.
- COLLABORATE: We value trust \+ transparency from everyone. Our goals are shared, our decisions data\-driven and our camaraderie genuine.
- BELONG: We recognize our differences, inviting curiosity and inclusivity, so that our people are valued, seen, and heard.
### How you'll make an impact:
Kymera is seeking a Senior/Executive Director, AI Strategy \& Applications to serve as the company's first dedicated AI leader. Reporting to the VP, Information Technology, this hands\-on builder will work collaboratively with Kymera's CEO and Executive Team to define Kymera's enterprise AI strategy and lead its execution. The person will own the selection and governance of AI platforms, and partner across Research, Clinical Development, Regulatory/CMC, and G\&A to turn scientific and business challenges into working AI solutions.
- Define and maintain Kymera's enterprise AI strategy and multi\-year roadmap, aligned to clinical and scientific priorities
- Prototype, build, and ship AI\-powered solutions, both progressing and adding to multiple existing initiatives, to meet urgent business needs
- Own evaluation, selection, and administration of AI platforms (LLM tools, AI\-assisted coding tools, scientific AI applications) across the enterprise, partnering with leadership to design and implement AI strategy for their functions.
- Establish and lead an AI governance function, partnering with Information Security on data privacy, acceptable use, and vendor risk policies
- Manage the AI vendor landscape and take ownership of deliverables from external AI engagements, determining what to operationalize or extend
- Champion AI adoption through education and enablement, partnering with People \& Culture on training programs and AI\-fluency hiring criteria
- Own Kymera's Answer Engine Optimization (AEO) / Generative Engine Optimization (GEO) strategy, ensuring AI platforms accurately represent Kymera's science and market position
- Build trusted relationships with functional leaders as a credible advisor on AI capabilities and limitations, from bench scientists to the C\-suite
### Skills and experience you'll bring:
- Experienced in biotech/pharma, with working knowledge of drug discovery, clinical development, regulatory, or CMC processes
- Hands\-on with modern AI/LLM platforms (e.g., OpenAI, Anthropic, Azure OpenAI), building solutions via APIs, prompt engineering, and agentic frameworks
- A strong software developer able to independently build and ship AI\-powered applications
- Skilled at translating complex business requirements into practical AI solutions
- Experienced managing vendor evaluations and enterprise AI platform deployments
- An excellent communicator, equally credible with technical and non\-technical stakeholders
- Preferred: experience in clinical\-stage/growth\-stage biotech, AI governance in a regulated environment, AEO/GEO familiarity, and comfort operating in a high\-autonomy, fast\-moving environment
*Equal Employment Opportunity*
Kymera Therapeutics is proud to be an equal opportunity employer, seeking to create a welcoming and diverse environment. All applicants will receive consideration for employment without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, pregnancy, sex, marital status, gender expression or identity, genetic information, sexual orientation, citizenship, or any other legally protected class.
*Compensation*
- Kymera offers a competitive compensation package that recognizes both results and capabilities through market\-based, performance\-driven pay.
- The anticipated base salary range for this role is $235,000 – $335,000, with eligibility for annual bonus, equity participation, and comprehensive benefits.
- Actual salary is based on a holistic evaluation of the specific role/level as well as each candidate's depth of experience and the capabilities they bring to the position.
Salary Context
This $235K-$335K 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 Kymera 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 ($285K) sits 30% above the category median. Disclosed range: $235K to $335K.
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.
Kymera Therapeutics AI Hiring
Kymera Therapeutics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Watertown, MA, US. Compensation range: $335K - $335K.
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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