Who are we?
🫀Vital builds software for care teams and patients, with a focus on the Emergency Room experience. We use machine learning to reduce length of stay, increase provider productivity, and improve patient satisfaction. Vital curates a better healthcare experience for patients and practitioners alike, saving millions of lives in the process.
💡 We provide hospitals with a modern UI and AI layer, which we are able to deploy within a matter of days.
🏥 There are more than 140 million visits to the ER every year in the US. Our mission is to improve the healthcare experience for these patients.
Who are you?We are looking for a Staff Machine Learning Engineer who specializes in infrastructure to found our MLOps team, reporting directly to the CTO.
You will be forming a small team tasked with the design, development and ownership of our modern machine learning platform. This platform provides the core functionality to train, evaluate, serve, and monitor the models at the heart of the Vital product. Machine learning is essential to the unique value that Vital provides its customers. Ensuring the quality, reliability, and efficiency of these systems is crucial to the success of this company!
This opportunity comes with significant scope to define the architecture of our platform as it evolves. We hope you will be passionate and opinionated about ML platform design, bringing battle-tested experience of patterns and software solutions which have succeeded and failed in the past.
We are looking for a candidate who is excited to be confronted with new challenges each day. The role comes with significant autonomy, and involves responding to the changing requirements of an early-stage startup. This is a senior role with significant ownership. We want you to feel that your work has a meaningful impact on a company which you partly own.
What will you be doing?
- Designing, developing and owning a machine learning platform serving on-line inferences to over 100 hospitals and thousands of patients
- Combining in-house development and implementation of best-in-breed SaaS solutions, enabling our platform to grow as we scale clients, models and data scientists
- Developing our in-house exploratory data analysis and research platform
- Developing automation tools across the ML development pipeline to increase efficiency and reliability
- Collaborating with our backend team in order to deliver ML predictions to production
- Collaborating with the rest of the data science team on prototyping and developing new data products
- Integrating with our data engineering team to make data available for research
Why work at Vital?
- We’re working on problems which have a real impact on people’s lives. When we succeed, patients get better care.
- Diversity, inclusion, and respect are important to us. Every team member must abide by our shared code of conduct.
- Every team member at Vital gets to share in our success in the form of stock options.
- We provide four weeks of annual leave for our team, ensuring you can take vacations as you need.
- We provide paid leave for new parents.
- We provide health insurance, and pay all the premiums.
Requirements
Need to have…
- Bachelor’s Degree in a STEM subject.
- 4+ years of experience with production Machine Learning systems and platforms
- 4+ years of software development experience in Python
- 2+ years experience leading a team building and supporting machine learning platforms hosted in the cloud
- Experience with UNIX-like environments
- Experience with modern software practices including automated testing and continuous delivery
- Experience with Pandas and NumPy
- Experience with machine learning libraries such as: scikit-learn, statsmodels, XGBoost, etc
- Experience with PyTorch, TensorFlow, JAX, MXNet, or another deep learning framework
- An ownership mentality, and the desire to take on heterogeneous challenges.
NOTE: The hours for this role overlap with our team in New Zealand. Specifically, we need you to be online from 9-11am New Zealand time. Of course, we’re happy for you to start your day later in order to accommodate.
Nice to have…
Some additional skills which we’d find useful if you have them, but should not affect your decision to apply:
- A Masters or PhD in ML, Data Science, or a related field
- Experience with AWS managed services for ML: SageMaker, Redshift, DynamoDB, Lambda, and Glue
- Experience with MapReduce frameworks such as: Spark, Scalding, Beam, Scio, Crunch, Pig, etc
- Experience with big data querying tools such as: Redshift, BigQuery, Hive, Presto, Mode, etc
- Experience with big data systems: Hadoop, Dataproc, Dataflow, EMR, Databricks, etc
- Experience with No-SQL data bases: DynamoDB, BigTable, Cassandra, Elasticsearch, MongoDB, etc
- Experience building classical ML models with: Scikit-Learn, statsmodels, XGBoost, etc
- Experience building deep learning models with: Keras, PyTorch, Tensorflow, MXNet, etc
- Published papers in ML or a related field
- Presented your work at a reputable scientific or technical conference
