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About This Role
Passionate about precision medicine and advancing the healthcare industry?
Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real\-world evidence to deliver real\-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time.
Tempus is on a mission to empower clinicians, care teams, and researchers by leveraging the unmatched power of data, AI, and technology to enhance patient care and advance precision medicine. To accomplish this, we've deeply integrated with more than 65% of U.S. academic medical centers, thousands of community institutions, 80\+ international sites, and built the world’s largest multimodal dataset of over 45mm records to power research and AI models.
As we scale we are seeking a SVP, Head of Growth for Apps to lead a dual\-sided growth strategy: driving commercial revenue within Life Sciences while simultaneously securing the high\-fidelity data pipelines and AI algorithm distribution channels with major Health Systems (Providers). Reporting directly to the CEO of Data \& Apps, you will be the commercial lead of our highly strategic business unit.
You will be a key catalyst to our growth by closing lighthouse strategic partnerships with large and respected academic medical centers while maintaining the "Builder" mindset required to solve the myriad of hurdles it will take to truly deploy AI at the point of care.
Responsibilities:
- Drive Commercial Success with Life Sciences
+ Lead the charge in selling our suite of AI applications (Next, TIME, \& Studies) to Biopharma partners to support RWE teams, clinical trials, and drug developers.
+ Responsible for making the distribution of AI a reality that Life Sciences partners can see and measure, proving that Tempus can meaningfully distribute AI diagnostics (Cardio, Path, etc.) at scale.
- Grow the Real\-time Connected Provider Network
+ Oversee the Connectivity partnerships team to secure real\-time EHR and med\-device integrations. Your goal is to double the size of our connected network each year and expand commercial rights to fuel long\-term growth.
+ Drive the adoption and clinical integration of AI diagnostics (e.g., ECG\-AI, Paige Predict). Ensure these tools are contracted, deployed, and utilized.
+ Design and execute large scale Strategic Partnerships that bundle applications, data sharing, and services into long\-term strategic alliances.
- Leadership \& Operations
+ Directly lead a 20\-FTE organization including Enterprise Provider Sales (Oncology, Cardio, Imaging, Intl) and Connectivity teams that manage our integrations with Providers. Manage dotted\-line relationships across NEXT, Studies, Cardio, and Trials.
+ Build out a commercial team selling to Life sciences across the Apps product lines.
+ Implement scalable, repeatable systems for pipeline management and accurate forecasting across both revenue and data\-acquisition KPIs.
Required Skills:
- You are a "Generalist High Tech" leader who has spent years in the healthcare/life sciences trenches. You can talk about AI abstraction with a CTO, clinical utility with a Chief Medical Officer, and real\-time data insights with a Chief Commercial Officer.
- You possess an "AI\-first" leadership mindset with proven experience translating deep technical understanding of AI, including Large Language Models (LLMs) and multimodal data, into successful commercialization strategies.
- You have 12\+ years of experience in enterprise sales or business leadership, with a track record of managing sales pipelines in excess of $100M\+.
- You are comfortable in a leadership role, but you haven't lost your "startup" agility. You thrive in a matrixed environment and can distill complex technical issues into concrete action plans.
- You understand that the future of healthcare relies on multimodal data. You understand the drug development lifecycle and the technical requirements of real\-time EHR connectivity.
Required Education \& Experience:
- 12\+ years in healthcare technology, life sciences services, or medical device sales, primarily at the CxO level.
- Proven success leading large, cross\-functional teams and managing complex, multi\-stakeholder deals.
- Bachelor’s degree in an analytical, technical, or healthcare\-related field (Advanced degree preferred).
- A dynamic sales execution mindset with a passion for precision medicine and a fast\-paced, evolving environment.
Pay Range: $330,000\.00 \- $430,000\.00 USD
The expected salary range above is applicable if the role is performed from Illinois and may vary for other locations (California, Colorado, New York). Actual salary may vary based on qualifications and experience. Tempus offers a full range of benefits, which may include incentive compensation, restricted stock units, medical and other benefits depending on the position.
We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
### About Us
Tempus was founded in August of 2015 by Eric Lefkofsky, after his wife was diagnosed with Breast Cancer. Shortly after he founded the company in an effort to bring the power of technology and artificial intelligence to cancer care, he convinced Ryan Fukushima to join as the company’s first employee. Ryan and Eric began assembling a world class team, focused on building the first version of a platform capable of ingesting real time healthcare data in an effort to personalize diagnostics.
We built the platform for oncology and have expanded it to neuropsychiatry, cardiology, infectious disease (through COVID), and radiology. Despite our rapid growth, our mission remains the same—to help make sure patients are on the right drug at the right time, so they can live longer and healthier lives.
### Why Work Here?
We’re looking for people who can change the world.
Who question the status quo and don’t shy away from tough problems. For the builders who are never done building and the learners who are never done learning. We’re looking for passionate people with undying curiosity. Those who want to attack one of the most challenging problems mankind has ever faced. Head on.
Salary Context
This $330K-$430K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Tempus, 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 $178,940 based on 11,900 positions with disclosed compensation. This role's midpoint ($380K) sits 112% above the category median. Disclosed range: $330K to $430K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Tempus AI Hiring
Tempus has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Chicago, IL, US, New York, NY, US. Compensation range: $150K - $430K.
Location Context
AI roles in Chicago pay a median of $202,000 across 283 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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