At Tempus AI, a Machine Learning Engineer plays a pivotal role in redefining how diseases like cancer are structured and treated. By leveraging the world's largest library of clinical and molecular data, you will build and deploy models that directly impact patient care, clinical trials, and therapeutic discovery. The work here sits at the intersection of high-performance computing, advanced biology, and production-grade software engineering.

The models you build do not exist in a vacuum; they power clinical decision support tools used by oncologists and researchers globally. This means your engineering decisions directly influence treatment pathways, drug discovery pipelines, and personalized medicine initiatives. Handling multimodal datasets—including genomic sequencing, pathology images, and unstructured clinical notes—requires a high level of technical rigor and creative problem-solving.

This role is highly critical because of the sheer scale and complexity of the biological data involved. As a Machine Learning Engineer, you will bridge the gap between cutting-edge research and scalable, real-world deployment. You will design pipelines that must be robust, reproducible, and compliant with stringent healthcare data standards, making this one of the most intellectually challenging and rewarding engineering roles in the technology sector.

The questions you will face during the Tempus AI interview process are designed to evaluate both your practical software engineering skills and your machine learning intuition. While these questions represent common patterns observed in real interviews, they are highly dependent on the specific team and the seniority of the role. Use them as a framework to guide your preparation rather than a list to memorize.

Algorithmic Foundations & Coding

These questions assess your ability to write clean, efficient, and bug-free code under time constraints during the initial technical screening.

• Write a function to solve a classic array manipulation problem with optimal time and space complexity.
• Implement a basic data structure, such as a queue or a tree traversal algorithm, from scratch.

Preparing for an interview at Tempus AI requires a balanced approach that combines strong software engineering fundamentals with deep machine learning intuition. You must be ready to demonstrate not just that you can build models, but that you can write production-grade code to deploy them safely.

Role-Related Knowledge – You must demonstrate a deep understanding of core machine learning concepts, including model evaluation, feature engineering, and optimization. For this role, familiarity with biological data structures, genomic sequencing concepts, and clinical data formats is highly advantageous. Interviewers will look for your ability to select the right tool for the specific data constraints you face.

Problem-Solving & Architecture – You will be evaluated on how you approach ambiguous, large-scale system design challenges. You need to show that you can break down a complex clinical problem into a clean, modular machine learning pipeline. This includes thinking critically about data ingestion, storage, processing, model training, and real-time or batch inference.

Execution & Engineering Best Practices – Tempus AI values engineers who write clean, maintainable, and reproducible code. You should be highly comfortable with containerization tools like Docker, version control, and writing comprehensive unit tests. Your code style, documentation, and ability to explain your architectural choices are just as important as the accuracy of your models.

Collaborative Communication – Working in healthcare AI requires constant collaboration with non-technical stakeholders, including medical doctors, molecular biologists, and product managers. You must be able to translate complex algorithmic decisions into clear, actionable business or clinical insights. Showing empathy, active listening, and structured communication is key.

The interview process for a Machine Learning Engineer at Tempus AI is designed to evaluate both practical execution and high-level architectural thinking. The process is thorough, highly technical, and deeply collaborative, ensuring that candidates possess both the engineering rigor and the scientific curiosity needed to succeed.

The typical journey begins with a recruiter screen to discuss your background, interest in the company, and general alignment with the role. Following this, you will transition to an initial technical screen, which often consists of an online coding assessment or a practical take-home assignment. The take-home assignment is a signature element of the Tempus AI hiring process, focusing on real-world tasks like analyzing a genomic dataset or containerizing an ML model.

Once you pass the initial screens, you will enter the panel interview stage, which may be conducted virtually or on-site. This stage involves deep-dive discussions about your take-home project, a machine learning system design round, and behavioral interviews focusing on your project history and communication style. The process is highly conversational, and interviewers actively encourage you to walk through your decision-making process in real time.

The timeline above illustrates the typical progression from the initial recruiter screen to the final panel rounds. Candidates should use this timeline to pace their preparation, focusing on coding and containerization in the early stages, and shifting toward system design and project presentation as they approach the panel interviews. While the overall structure remains consistent, the depth of the system design and presentation rounds may scale depending on whether you are interviewing for a standard, Senior, or Staff level position.

To succeed in the Tempus AI interview process, you must master several key technical and behavioral domains. Each of these areas represents a core pillar of the day-to-day work within the clinical AI team.

Take-Home Project & Dockerization

The take-home project is your opportunity to showcase your hands-on engineering skills. Tempus AI values reproducible research, which means your code must run seamlessly in any environment. You will be evaluated on your ability to package your solution cleanly and document your methodology.

Be ready to go over:

• Containerization with Docker – Writing clean, optimized Dockerfiles and ensuring your environment is fully reproducible.
• Model Evaluation – Choosing the correct metrics to evaluate model performance on clinical datasets and explaining your choices.
• Code Quality & Structure – Writing modular, readable Python code with proper error handling and clear documentation.
• Advanced concepts (less common) – Multi-stage Docker builds, optimizing image sizes, and writing automated test suites for ML pipelines.

Example scenarios:

• "You are handed a raw genomic dataset and a pre-trained model. Create a Dockerized application that ingests the data, runs inference, and outputs a structured JSON report of the findings."
• "Your take-home model performs well on the test set but fails when packaged in Docker. Debug the dependency conflicts and optimize the container run time."

Machine Learning Pipeline Design

This area evaluates your ability to design scalable, production-grade systems that process massive amounts of clinical and molecular data. You must show that you understand the lifecycle of a model from ingestion to monitoring.

Be ready to go over:

• Data Ingestion & Processing – Handling batch and streaming data, feature extraction, and managing large-scale genomic files.
• Model Deployment Strategies – Choosing between real-time API endpoints and batch processing workflows based on clinical needs.
• Monitoring & Observability – Tracking model drift, data quality, and system latency in a live healthcare environment.
• Advanced concepts (less common) – Feature stores, distributed training with PySpark, and implementing zero-downtime model deployments.

Example scenarios:

• "Design a system that receives real-time pathology images from hospital clients, processes them through a deep learning model, and returns predictions to the physician's portal within five seconds."
• "Architect a pipeline to update clinical risk prediction models weekly as new electronic health record (EHR) data is ingested."

Algorithmic Problem Solving & Coding

Before designing complex pipelines, you must demonstrate strong coding fundamentals. This is typically evaluated through an online coding assessment or a live coding round.

Be ready to go over:

• Data Manipulation – Efficiently filtering, grouping, and transforming large datasets using Python and standard libraries.
• Algorithm Optimization – Identifying performance bottlenecks and optimizing time and space complexity.
• Basic Data Structures – Utilizing hash maps, trees, and queues effectively to solve algorithmic challenges.
• Advanced concepts (less common) – Custom tensor operations, custom loss function implementations, and memory-mapped file processing.

Example scenarios:

• "Write an efficient algorithm to find overlapping genomic intervals within a large database of genetic mutations."
• "Implement a custom data loader that streams data from disk without running out of memory during training."

Scientific & Behavioral Communication

At Tempus AI, machine learning engineers work closely with scientists, doctors, and product managers. Your ability to communicate complex mathematical and engineering concepts to diverse audiences is highly prioritized.

Be ready to go over:

• Project Retrospectives – Explaining past projects, including the technical trade-offs, failures, and key learnings.
• Handling Ambiguity – Demonstrating how you make progress when requirements are incomplete or data is messy.
• Cross-Functional Collaboration – Showing how you align with non-technical stakeholders to deliver clinical value.
• Advanced concepts (less common) – Explaining complex deep learning predictions (explainable AI) to medical professionals.

Example scenarios:

• "Describe a situation where a clinician questioned your model's prediction. How did you explain the model's logic and build trust with them?"
• "Walk me through a time when a project's requirements changed mid-way through development. How did you adapt your engineering design?"