Company Description
By the end of 2020, contingent workers made up nearly 40% of the US workforce, and that number is growing steadily year after year.
At Sense, we are reinventing the way staffing agencies build long-term relationships with their workforce. We obsess over the candidate experience, always looking for ways to improve how staffing agencies communicate and engage with talent before, during and after each assignment. Sense is a rapidly growing company, and our engineering team is the best environment for people looking for challenges, ownership, and learning opportunities.
Job Description
Our customers say that finding the right candidates at the right time can be like finding a needle in a haystack. Sense, however, makes the whole process a lot less labor intensive for them. Agencies using Sense unlock the real power of recruiters, who don’t have to spend most of their time managing mechanical and repetitive tasks.
One of the most powerful tools in our portfolio is the virtual assistant, which we call Reva (Recruiting Virtual Assistant). Reva proactively reaches out to candidates to ensure their information is as accurate as possible, pre-screens them, schedules interviews with the recruitment team, and does a lot more.
The assistant is the first of many projects that relies on Machine Learning to make our products more intelligent and powerful, and we need your help to make our vision a reality. If you have experience building reliable and scalable infrastructure and are passionate about ML, then this is the perfect opportunity for you!
In this role, you’ll work at the intersection of Machine Learning, DevOps and Data Engineering, and you’ll be responsible for deploying and maintaining ML systems in production reliably and efficiently.
Qualifications
Responsibilities:
Help design and develop novel ML systems infrastructure within Sense
Design and build scalable automation pipelines to train, test, and deploy ML models for a variety of AI products
Given requirements around availability, scalability, performance, observability and recoverability, come up with complete designs for ML platforms in staging and production environments
Given requirements from our Data Science team around experimentation and verification, build on-demand services to improve their productivity
Hold reusability, extensibility, right separation of concerns in high importance while designing at data & service levels
Own quality & performance “measures” of service/application, and work towards constantly & consistently improving on the same.
Mentor other engineering team members through code reviews, pair programming etc.
About You:
Bachelor’s degree in computer science or other highly technical scientific discipline
4+ years of experience with AWS services
2+ years of experience with deployment systems like Jenkins, CI/CD pipelines.
Experience with distributed storage technologies like NFS, HDFS, Ceph, S3 as well as dynamic resource management frameworks (Mesos, Kubernetes, Yarn)
Familiarity with Machine Learning or NLP technologies is a plus
Familiarity with Amazon SageMaker is a plus
Additional Information
All your information will be kept confidential according to EEO guidelines.
By the end of 2020, contingent workers made up nearly 40% of the US workforce, and that number is growing steadily year after year.
At Sense, we are reinventing the way staffing agencies build long-term relationships with their workforce. We obsess over the candidate experience, always looking for ways to improve how staffing agencies communicate and engage with talent before, during and after each assignment. Sense is a rapidly growing company, and our engineering team is the best environment for people looking for challenges, ownership, and learning opportunities.
Job Description
Our customers say that finding the right candidates at the right time can be like finding a needle in a haystack. Sense, however, makes the whole process a lot less labor intensive for them. Agencies using Sense unlock the real power of recruiters, who don’t have to spend most of their time managing mechanical and repetitive tasks.
One of the most powerful tools in our portfolio is the virtual assistant, which we call Reva (Recruiting Virtual Assistant). Reva proactively reaches out to candidates to ensure their information is as accurate as possible, pre-screens them, schedules interviews with the recruitment team, and does a lot more.
The assistant is the first of many projects that relies on Machine Learning to make our products more intelligent and powerful, and we need your help to make our vision a reality. If you have experience building reliable and scalable infrastructure and are passionate about ML, then this is the perfect opportunity for you!
In this role, you’ll work at the intersection of Machine Learning, DevOps and Data Engineering, and you’ll be responsible for deploying and maintaining ML systems in production reliably and efficiently.
Qualifications
Responsibilities:
Help design and develop novel ML systems infrastructure within Sense
Design and build scalable automation pipelines to train, test, and deploy ML models for a variety of AI products
Given requirements around availability, scalability, performance, observability and recoverability, come up with complete designs for ML platforms in staging and production environments
Given requirements from our Data Science team around experimentation and verification, build on-demand services to improve their productivity
Hold reusability, extensibility, right separation of concerns in high importance while designing at data & service levels
Own quality & performance “measures” of service/application, and work towards constantly & consistently improving on the same.
Mentor other engineering team members through code reviews, pair programming etc.
About You:
Bachelor’s degree in computer science or other highly technical scientific discipline
4+ years of experience with AWS services
2+ years of experience with deployment systems like Jenkins, CI/CD pipelines.
Experience with distributed storage technologies like NFS, HDFS, Ceph, S3 as well as dynamic resource management frameworks (Mesos, Kubernetes, Yarn)
Familiarity with Machine Learning or NLP technologies is a plus
Familiarity with Amazon SageMaker is a plus
Additional Information
All your information will be kept confidential according to EEO guidelines.