$80K — $100K *
ABOUT DEEP LABS
We Believe Persona-Based Intelligence is the Next-Generation of True Context-Aware Computing. Deep Labs teams are removing the constraints of static, rule-based decisions. Our leading cutting-edge data science, behavioral modeling, and massive data sets enable delivery of persona-based intelligence to the biggest names in finance, government, and retail.
We look for individuals who dream big, work hard, and above all stay humble. Trust and collaboration is at the heart of what we do, and through our work together we hope to create a supportive, welcoming, and innovative environment.
WHAT YOU’LL DO & IMPACT YOU’LL HAVE
As a Senior ML Operations Engineer, reporting to the VP Data Science & Engineering, you'll speak up, solve problems, lead others, and be an owner in your role. If you're passionate about building well-oiled data and machine learning flows with process quality, automation, scale and product focus as key tenets, are opinionated on design, and prefer to take ownership of tasks – you are right for this role. If you understand that documentation is the heart of everything and are always the one to point out that more time should be spent on it, we want you!
The Top Five Objectives for this role (plus some bonus ones) include:
1. Own & drive innovation of the end-to-end machine learning production deployment process
2. Build & manage processes to deploy production ML models in batch and real-time inference environments, and monitor the performance and quality of those models over time
3. Develop strategies and solutions to train and test machine learning models at-scale
4. Help to develop and manage governance processes for data, models, and documentation
5. Work closely with data scientists to help transition models to production
+ Work collaboratively to support your peers and enable team wins
+ Assist with various cross-functional initiatives to further company success
+ Act as an ambassador for Deep Labs values, culture, including fostering inclusion and belonging, with attention toward well-being - We win together!
SOUND LIKE YOU? YOU MAY BE THE RIGHT FIT IF YOU HAVE:
1+ years experience in a dedicated ML Ops role, with exposure to complex Data Science/Machine Learning problems in a production context
Experience working with large structured & unstructured data in various formats
Strong proficiency in all of: Python 3.x, Linux, Docker
Experience using open-source machine learning and deep learning frameworks (e.g. scikit-learn, xgboost, Tensorflow/Keras, SparkML, Databricks).
Experience working in public cloud environments (Google Cloud or AWS preferred)
Strong understanding of SDLC best practices, and experience applying them in large software delivery projects
Strong analytical and quantitative skills; strong attention to detail
Knowledge and experience of one or more of the following is a plus:
data processing frameworks & libraries (e.g. Pandas, Dask, PySpark)
graph data models & graph processing algorithms
orchestration & data management tools (e.g. Apache Airflow, Kafka)
container orchestration (e.g. Kubernetes)
notebooks (Jupyter and/or Databricks), and the wider notebook ecosystem
event streaming frameworks (e.g. Apache Beam, Flink or Spark Streaming)
+ Desire to make a meaningful impact on our growth!
+ You enjoy the BUILD! You enjoy startup PACE! You enjoy SCALE!
+ Ability thrive in autonomous, sometimes ambiguous environments
+ And, of course, you love a good challenge!
Valid through: 12/15/2021