Staff Software Engineer
Mumbai, India
Nielsen
A global leader in audience insights, data and analytics, Nielsen shapes the future of media with accurate measurement of what people listen to and watch.ABOUT THIS JOB
Nielsen Global Media uses cutting edge technology and industry leading data science to tackle some of the hardest problems in marketing science. We’re automating our models with artificial intelligence and machine learning to produce the same quality insights as a traditional white-glove consulting engagement at unparalleled speed and scale.
Intelligence Studio is a horizontally scalable, cross-cloud technology agnostic platform built with trusted open source components like VS Code, Apache Airflow, Jupyterhub and MLFlow. It allows data scientists to focus on doing data science by taking care of essential concerns like data access, logging, configuration, resource negotiation, dependency management, orchestration, and testing.
We’re looking for a Staff Software Engineer to help our talented, cross-functional team improve user workflows in Intelligence Studio. Ideal candidates will be hands-on technologists with experience in Python, Kubernetes, Distributed Systems, AWS or Azure cloud infrastructure. This position is a fantastic opportunity for an experienced engineer to work with creative engineers and cutting-edge technologies.
RESPONSIBILITIES
- Build software and integrations in a cloud-based microservices environment (kubernetes) for big data applications with Spark, Ray, etc.
- Writing software in python, typescript, go, Java and Scala
- Work with stakeholders and technical leadership to design and build interfaces, workflows, and services that enhance the delivery of data science products
- Actively participate in team code reviews and enforce quality standards
- Work within a cross-functional team to author clear and purposeful epics/stories
- Promote and enforce best practices in development and operations
- Identify opportunities and weaknesses in the platform architecture
- Design and develop data visualization tooling using electron, jupyterhub, plotly, typescript and pandas to enhance data exploration workflows for data science
- Integrate data science visualization and diagnostic tooling like tensorboard, ray serving, spark history server into an existing distributed compute and development environment
- Build secure integrations with the kubernetes api allowing the management of user workloads in a shared environment with potentially sensitive data
- Understand and debug interactions between cloud networking components (ALBs, web api proxies) cluster ingression and security using kong and istio, python-based web servers and modern web transfer protocols like websockets and http3.
A LITTLE BIT ABOUT YOU
- You are an experienced software engineer with a proven track record of quickly learning and implementing new technologies. You love technology and are excited to work on a high-performance team building a very ambitious product. You are looking for an opportunity to grow your career and your technical depth by diving into a project working on the current state of the art in big data and cloud technologies.
QUALIFICATIONS
- Bachelor’s degree in Computer Science or a related technical field, or equivalent industry experience
- Typescript, Python, Kubernetes, Airflow, Electron, Jupyter, Pandas, Keras, ray, tensorflow, CUDA
- Apache Spark, Istio, Scala, Java, Go, kong, cloud software design, containerized microservices & distributed caching.
- Experience with machine learning (RNNs, CNNs, random forest, LLMs) a plus
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Airflow APIs Architecture AWS Azure Big Data Computer Science Consulting CUDA Data visualization Distributed Systems GloVe Java Jupyter Keras Kubernetes LLMs Machine Learning Microservices MLFlow Open Source Pandas Plotly Python Scala Security Spark TensorFlow Testing TypeScript
Perks/benefits: Career development
More jobs like this
Explore more career opportunities
Find even more open roles below ordered by popularity of job title or skills/products/technologies used.