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About This Role
Join Immatics and shape the future of cancer immunotherapy; one patient at a time!
Immatics is committed to making a meaningful impact on the lives of patients with cancer. We are the global leader in precision targeting of PRAME, a target expressed in more than 50 cancers. Our cutting-edge science and robust clinical pipeline form the broadest PRAME franchise with the most PRAME indications and modalities, spanning TCR T-cell therapies and TCR bispecifics.
Why Join Us?
- Innovative Environment: Help to pioneer advancements in cancer immunotherapy.
- Collaborative Culture: Be part of a diverse team dedicated to your professional growth.
- Global Impact: Contribute to therapies that make a lasting impact on patients globally.
Role Overview
We are seeking a Senior Director, Commercial Forecasting & AI combined role. Approximately half of the position is owning demand, revenue and patient-flow forecasting across Immatics’ portfolio — from early pipeline opportunity assessment through launch readiness and in-line performance. The other half is serving as the Commercial organization’s in-house AI expert: identifying, building and scaling AI and generative-AI capabilities that make a small commercial team perform like a much larger one. The successful candidate is a recognized forecasting expert in oncology or cell and gene therapy who has already put AI to practical, compliant use in a life sciences commercial setting.
Reports to: VP Commercial Analytics & Operations
Location: Remote U.S.
Salary Range: $240,000 – $280,000
Basic Qualifications:
- Bachelor’s or equivalent years of experience
- 15+ years in Life sciences consultancy, biotech, pharma or related
Preferred Qualifications:
- 7 Years of Forecasting, Commercial Analytics or market insights
- Demonstrated expertise building patient-based and epidemiology-driven forecast models from the ground up for both pre-launch and in-line assets.
- Deep working knowledge of pharma/biotech data sources and their strengths and limitations — e.g. IQVIA, Symphony Health, Komodo, Definitive Healthcare, specialty distributor and 867/852 data, SEER and other epidemiology sources, clinical registries and syndicated market research.
- Demonstrated, hands-on experience applying AI and large language models to real commercial problems in a regulated life sciences environment — including practical fluency with LLM platforms, prompt engineering, retrieval-augmented generation and agentic workflows, and a clear-eyed view of where AI does and does not work.
- Working understanding of the compliance and privacy constraints on AI in life sciences (PHI/HIPAA, GDPR, promotional review, data licensing terms, emerging AI regulation).
- Advanced Excel modeling plus working proficiency in Python, SQL and a visualization tool (Power BI/Tableau).
- Experience across the launch lifecycle in oncology, cell and gene therapy, rare disease or another specialty/limited-distribution setting.
- Proven ability to operate independently in a lean, fast-moving environment, and to translate complex quantitative and technical work into decision-ready recommendations for senior stakeholders.
- Direct experience forecasting an autologous CAR-T, TCR-T or other engineered cell therapy asset, including site activation and manufacturing-constrained demand models.
- Track record of standing up a forecasting or commercial AI capability at a pre-commercial or newly commercial company.
- Exposure to global forecasting and to market access/pricing analytics in specialty oncology.
In this role you will:
- Develop Commercial AI roadmap — Serve as the Commercial team’s resident AI expert across the full commercial organization: partner with Marketing, Market Access, Medical Affairs and Field Operations to identify, prioritize and sequence high-value AI and generative-AI use cases — analytics and forecasting, market research synthesis, competitive intelligence, field enablement, content development and promotional review preparation, account and KOL intelligence, payer and policy monitoring, and CRM data quality — with a clear view of business impact, effort and sequencing.
- Build the operating systems — Design, build and own AI-powered systems that become how Commercial runs day to day — not one-off pilots. Re-engineer the analytics workflow end to end so recurring work runs itself: automated ingestion and QC of claims, specialty distributor, CRM and third-party data; reconciliation and exception flagging; forecast and dashboard refreshes; self-service analytics layers; recurring report and insight generation; and agentic workflows that take manual assembly off the critical path — shifting the team’s time from producing numbers to interpreting them.
- Build vs. buy and vendors — Evaluate AI platforms and vendors, run structured pilots against defined success criteria, and make defensible build-versus-buy recommendations.
Travel Requirements:
- 10-15%
What do we offer?
At Immatics, we believe in investing in our team’s health, safety and well-being. Here’s what you can expect if you join Immatics
Comprehensive Benefits:
- Competitive rates for Health, Dental, and Vision Insurance
- 4 weeks of vacation, granted up front each year and prorated for first and last year of employment.
- 12 company paid holidays
- 7 days of sick time
- 100% employer-paid life insurance up to at 1x annual salary, up to one hundred thousand dollars
- 100% employer-paid short- and long-Term disability coverage
- 401(k) with immediate eligibility and company match. The company will match 100% of your contributions up to 3% of your base salary for the first two years of employment, 4% for years 2-3 of employment, and up to 5% of your salary from the fourth year onward of continued employment.
- Partially paid parental leave for eligible employees.
Additional voluntary employee-paid benefits and services, including accident, hospital indemnity, and critical illness insurance, as well as identity theft protection and pet insurance. *
Equal Employment Opportunity
We are an equal opportunity employer and are committed to building a diverse and inclusive workplace. We consider all qualified applicants for employment without regard to race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity or expression, national origin, ancestry, age, marital status, disability, genetic information, veteran status, or any other status protected by applicable law.
Reasonable Accommodations
We are committed to providing reasonable accommodations to individuals with disabilities and to applicants with sincerely held religious beliefs, practices, or observances. If you require assistance or accommodation during the application or interview process, please contact us at [email protected].
Work Authorization
Applicants must be authorized to work in the United States without the need for current or future sponsorship.
Pre-Employment Requirements
Employment is contingent upon successful completion of a background check, reference checks and pre-employment drug screening, where permitted by applicable law. For certain roles, additional screenings such as a credit check or motor vehicle record review may be required, where job-related and consistent with business necessity.
Salary Context
This $240K-$280K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Immatics US, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($260K) sits 23% above the category median. Disclosed range: $240K to $280K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Immatics US AI Hiring
Immatics US has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US. Compensation range: $280K - $280K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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