Hyperscience modernizes mission-critical processes and operations for the world's largest organizations and government agencies. Since 2014, Hyperscience’s automation technology has helped data-centric companies parse through vast amounts of unstructured inputs and raw information to get to swifter and smarter business outcomes. Through the Hyperscience Platform, enterprises are empowered to transform their operations, and drive operational efficiency as well as human productivity by fully unlocking the power of their data.
Ranked on the Inc. Fastest-Growing Company List, Hyperscience has raised $190M from investors including Tiger Global, BOND, Bessemer Venture Partners, Stripes, and FirstMark. The company has consistently been recognized as one of the best places to work with a collaborative and innovative culture and best-in-class benefits.
The company has a global footprint with offices in New York City, Sofia, Bulgaria, Toronto, Canada, and London, UK.
As a Machine Learning Ops (MLOps) engineer, you will be responsible for building and maintaining the next generation of Hyperscience’s ML Platform and Infrastructure. You will lead initiatives geared towards making the ML Engineers and Applied Scientists at Hyperscience more productive. You will make improvements to and extend the underlying infrastructure that powers the ML teams, thus simplifying the development and deployment cycles of ML models. You will help establish best practices for the ML pipeline and partner with other infrastructure ops teams to help champion them across the company.
- Design and build effective, user-friendly infrastructure, tooling, and automation to accelerate Machine Learning at Hyperscience
- Collaborate with teams to drive the ML technical roadmap
- Collaborate with Machine Learning Engineers and Product Managers to develop tools to support experimentation, training and production operations
- Build and maintain data pipelines using tools like Hadoop, Python, Airflow, and Kafka
- Offer support and troubleshooting assistance for the ML pipeline, while continuously improving stability along the way
- Build and maintain systems employing an Infrastructure-as-Code approach
- Own the AWS stack which comprises all ML resources
- Establish standards and practices around MLOps, including governance, compliance, and data security
- Collaborate on managing ML infrastructure costs
- 1+ years of experience with ML infrastructure and ML DevOps
- 4+ years of overall engineering experience in distributed systems and data infrastructure
- 3+ years experience coding in Python (preferred) or other languages like Java, C#, Golang etc.
- Experience working with ML engineers to build tooling and automation to support the entire ML engineering lifecycle, from experimentation to production operations
- Experience with Kubernetes and ML CI/CD workflows
- 3+ years experience with AWS or other public cloud platforms (GCP, Azure, etc.)
- Excellent verbal and written communication skills.
Nice to haves
- Experience with Infrastructure-as-Code tools and frameworks
- Top notch healthcare for you and your family
- 30 days of paid leave annually to help nurture work-life symbiosis
- A 100% 401(k) match for up to 6% of your annual salary
- Stock Options
- Wellness stipend
- Pre-tax transportation and commuter benefits
- 6-month parental leave (or double salary to pay for your partner's unpaid leave)
- Free travel for any person accompanying a breastfeeding mother and her baby on a business trip
- A dependent care stipend up to $3,000 per month, per child, under the age of 21 for a maximum of $6,000 per month total
- Daily catered lunch, snacks, and drinks
- Budget to attend conferences, train, and further your education
- $1,000 one-time-use WFH stipend and $75 monthly WFH stipend
- Relocation assistance
We are an equal opportunity employer. We welcome people of different backgrounds, experiences, abilities and perspectives. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status.