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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.
We built Tempus to collect, structure, and organize data from disparate sources to power innovation and discovery. And now we are looking for an Associate Key Account Director to join our rapidly growing Life Sciences team. This role will focus on creating and expanding our global client relationship with some of the world’s leading biopharmaceutical organizations.
The Associate Key Account Director is responsible for cultivating and growing Tempus partnerships with pharmaceutical and biotechnology clients to support them in advancing their research, clinical and/or commercial programs. The Tempus precision medicine platform: Data, Artificial Intelligence, Sequencing and Clinical Trials is key to the future of drug development at scale where personalized medicine is something that we all enjoy. The Associate Key Account Director will work cross\-functionally with Tempus stakeholders to establish Tempus as the Precision Medicine Partner for their accounts.
Responsibilities
- Identify high potential customer contacts at biopharma companies based on alignment with Tempus offerings and commercial strategy
- Conduct outreach to executives at life sciences companies to open partnering discussions
- Identify customer needs and effectively communicate the value proposition of Tempus product lines
- Support development of global account strategy in coordination with Key Account Directors, scientific teams and business operations teams
- Contribute to meeting and exceeding revenue goals across Tempus product lines at accounts while supporting delivering on established strategic objectives for specific client accounts
- Support Key Account Directors in negotiating, securing and managing pull\-through of contracts with assigned client accounts.
- Align the cross functional team of Product Specialists, Alliance Managers, Translational Researchers and Commercial Operations to most efficiently identify and deliver value at our clients
- Contribute to the development of the Life Sciences portfolio by providing feedback to Leadership regarding client responses and suggestions; maintain a solution\-oriented mindset
- Travel approximately 20% of working time, domestically and internationally
- Other duties as assigned
Qualifications
- Entrepreneurial sales approach; thrives most in a high growth, rapidly evolving business environment
- Proactive mindset that bridges ideas to implementable solutions, and can help move both internal and external stakeholders to quickly solve problems
- Effective collaborator who is able to work with a variety of internal and external stakeholders
- Ability to set strategies/tactics that are aggressive, but realizable
- Wins followers with a positive and energetic approach to work and life; gains energy from solving difficult problems
- Demonstrated ability to influence without authority (both with internal and external stakeholders)
- Learning agility to adapt to a rapidly changing environment within Tempus
Experience
- Minimum 5\+ years in business development or Sales in the life sciences sector (Pharma / Biotech)
- Preferably 2\+ years working with companies who serviced Life Sciences companies in research and development, real world data or molecular diagnostics
- Proven track record of establishing and building close customer relationships
- Understanding of real world data, molecular diagnostics and the life sciences drug development process
- Bachelor or advanced degree in a Science or Business discipline
\#LI\-REMOTE
\#LI\-NK1
$90,000\-$140,000
The expected salary range may vary for other locations. 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 $90K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($115K) sits 46% below the category median. Disclosed range: $90K to $140K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Tempus AI Hiring
Tempus has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $140K - $140K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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