Tempus
Prior Listings
- Machine Learning Engineer (NLP)
- Machine Learning Engineer, MLOps
- Analytics Manager, Translational Research
- Real World Data Scientist
- Staff Machine Learning Scientist, Generative AI
- Real World Data Scientist
- HR Data Analyst, People Team
- Staff Machine Learning Scientist (Cardio)
- Real World Data (RWD) Scientist
- Bioinformatics Analyst - Quality Control/Variant Interpretation
- Data Analyst, Commercial Feasibility Analytics
- Director, Data Science Operations
- Machine Learning Scientist, Generative AI
- Machine Learning Scientist, Generative AI
- Data Scientist II, Outcomes Research
Machine Learning Engineer, MLOps
Machine Learning Engineer, MLOps
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 are looking for a Machine Learning Engineer who is passionate about helping teams build, validate, and deploy machine learning models at scale, maintaining performance throughout the model lifecycle, and building tools to provide model explainability and transparency.
What You'll Do
- Collaborate with data science, product, research, and engineering teams to translate prototype models into production clinical alogrithms
- Design and build solutions for model validation, monitoring, and explainability using custom, open source, and commercial software
- Support models in production and assist model owners with troubleshooting performance issues
- Document and communicate your solutions to teams of users and stakeholders
Qualifications
- Degree in computer science, software engineering, statistics, machine learning, bioinformatics or related technical field
- A combination of 3+ years full time professional experience both:
- Building and validating predictive models on structured or unstructured data
- Deploying, scaling, monitoring and/or troubleshooting ML models in production
- Experience with the following: Pandas, NumPy, SciPy, Scikit-learn, Jupyter Notebooks
- Proficient in Python, SQL, Git, Docker
- Experience working in a Linux / Mac environment and with cloud computing services
- Experience with supervised and unsupervised machine learning algorithms, and ensemble methods, such as: K-Means, PCA, Regression, Neural Networks, Decision Trees, Gradient Boosting
- Outstanding programming and problem solving skills
- Self-driven and work well in an interdisciplinary team with minimal direction
- A strong desire to understand why things work the way they do
- Thrive in a fast-paced environment and willing to shift priorities seamlessly
Nice to Haves
- Experience with GCP architecture, especially Vertex AI/Kubeflow/TFX
- Experience with: Flask, Kubernetes, Terraform, Concourse
- Experience in agile environments and comfort with quick iterations
- Experience working with clinical and/or genomic data