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About This Role
Alumis Inc. is a precision medicines company with the mission to transform the lives of patients with autoimmune diseases. Even with treatment innovations of the last two decades, many patients with immunologic conditions continue to suffer - our goal is to fundamentally change the outcomes for these patients.
We have recently completed our pivotal trials of envudeucitinib, our second-generation tyk2 inhibitor in moderate to severe plaque psoriasis and expect results from a potentially pivotal Phase 2b trial in SLE in the third quarter of 2026.
As a key member of the Alumis team, you will provide overall leadership for the Medical Affairs function, overseeing Medical Affairs, Field Medical Liaisons, and publications. The position will liaise closely with the clinicians responsible for carrying out Alumis' clinical studies as well as our commercial group. Applicants should be a "leader/doer" capable of generating strategies and leading cross functionally. This VP, Medical Affairs will report directly to the Chief Medical Officer.
Responsibilities
Lead the Medical Affairs function at Alumis
- Develop and implement key Medical Affairs strategies and activities to support the development of our lead assets
- Continue to build and scale the Medical Affairs organization
- Develop and present Medical Affairs operational plans to Development and Executive management
- Represent company externally
- Direct and oversee all aspects of Medical Affairs and pre-launch needs
- Lead medical launch preparations for our lead Psoriasis candidate, ensuring the scientific narrative is robust and well-vetted.
As a member of the Development Leadership Team, run and continue to build the Medical Affairs function
- Build and lead a best-in-class global medical affairs team to support launches of multiple indications with a focus on psoriasis for our lead asset, envudeucitinib
- Develop key performance indicators and generate analytical reports related to Medical Affairs plans and Medical Science Liaison group activities
- Develop and implement Medical Communication and Disease State Awareness strategies
- Partner with KOLs to gather information on current focused therapeutic area issues and questions
- Collaborate with the Commercial team to develop educational material for providers and patients that are balanced and medically accurate
- Provide direction and input into the development and implementation of successful reimbursement and market-access strategies
- Work closely with the members of the clinical development and commercial leadership teams to develop the overall strategic direction for Alumis, evaluate alternative strategies, identify competitive issues, and develop and implement operating plans to achieve company objectives
- Foster collaborative relationships with academic and clinical experts, publishers, medical and patient associations, and other relevant external and internal stakeholders
- Develop and manage departmental budgets that effectively achieve desired goals and are balanced with the financial objectives of the broader organization
- Oversee the publication strategy, ensuring clinical data is presented at major congresses (e.g., AAD, EADV, ACR) and in high-impact peer-reviewed journals.
Education | Experience | Skill Requirements
- 10+ years of professional experience within Biotech/Pharma in Medical Affairs
- 5+ years of people leadership experience
- An understanding of government and industry guidelines, regulations, laws, etc., for appropriate scientific/medical exchange and communication with customers
- Ability to lead by example, attract and develop talent, build interdependent partnerships, and create a culture of collaboration and teamwork that fosters open communication, constructive conflict resolution and organizational flexibility
- Ability to prioritize and manage several complex projects simultaneously
- Proven ability to develop internal relationships in a highly matrixed environment, as well as external relationships with Key Opinion Leaders and industry experts
- Ability to communicate effectively at all levels and present complex and/or new ideas with clarity and simplicity
- Ability to prioritize concurrent projects with tight deadlines and operate in a highly compliance-driven environment
- Excellent written, verbal, and interpersonal skills, and the ability to communicate at all levels within the company
- Travel (25-40%) may be required to represent the company at medical conferences, presentations, and other meetings
- Our headquarters are located in the San Francisco Bay Area and regular presence at headquarters is required
- Extensive prior experience in dermatology, particularly psoriasis is required. Prior experience in SLE is highly desirable. Prior experience taking a product form Phase 3 to a successful launch is also highly desirable.
- Advanced degree (PharmD, Ph.D. or MD) is required, MD is preferred.
*The salary range for this position is $315,000 USD to $370,000 USD annually. This salary range is an estimate, and the actual salary may vary based on the Company's compensation practices.*
Salary Context
This $315K-$370K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Alumis, 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 $154,000 based on 8,743 positions with disclosed compensation. This role's midpoint ($342K) sits 122% above the category median. Disclosed range: $315K to $370K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Alumis AI Hiring
Alumis has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco Bay Area, CA, US. Compensation range: $370K - $370K.
Location Context
AI roles in San Francisco pay a median of $240,000 across 1,230 tracked positions. That's 26% above the national 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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