The job, in the employer's words
About the company
Our client is a software company.
The role
Raydar is recruiting for this role on behalf of our client. Build and protect the cloud and data foundations of an enterprise software platform that works with sensitive information. Collaborate with research and product colleagues to support machine learning workloads and keep releases dependable across several cloud environments.
What you'll do
- Design and build scalable infrastructure for handling both structured and unstructured data.
- Shape a secure-by-default setup for privately hosted deployments that satisfies data governance expectations.
- Build dependable ingestion pipelines that draw from external feeds and internal document repositories.
- Create monitoring and alerting for customer-hosted environments.
- Set up permissions and audit logging so system outputs can be traced to their origins.
- Work with research and product colleagues to tune infrastructure for model training, inference and agent-based systems.
- Introduce automated delivery workflows and codified infrastructure to make releases fast and repeatable across multiple cloud providers.
Requirements
What we're looking for
- 3 to 10 years of experience in infrastructure or platform engineering.
- Background at a company known for a demanding engineering standard, ideally on an early team.
- Deep hands-on knowledge of a leading public cloud, plus running containerized workloads on Kubernetes at scale.
- A profile that fits either a cloud infrastructure engineer working across providers or a data infrastructure engineer building complex ingestion pipelines.
- A history of quick growth in title or scope.
- Working familiarity with data pipeline tools and real-time processing, with cloud infrastructure as the primary strength.
- A careful, security-first mindset and practical exposure to data governance.
- Comfort with observability, logging and monitoring tooling.
- Awareness of common regulatory and audit frameworks.
- A degree from a top 25 Computer Science program.
- Genuine motivation to join an early-stage startup and the ability to move quickly while building sturdy systems.
- Candidates based in New York generally need 4 to 5+ years of experience, unless they have strong startup experience or a top 25 Computer Science degree.
Bonus points
- Exposure to machine learning infrastructure.
- Experience in regulated industries.
- Experience with managed AI platforms.
- Knowledge of financial services regulations.
Benefits
Compensation and benefits
- Base salary: USD 150,000 to 300,000 per year
- Equity
- Health, dental and vision coverage
- Retirement plan with employer match
- Unlimited paid time off
Location and work model
- San Francisco, CA, United States or New York, NY, United States
- On-site, 5 days per week in office
- Full-time
Benefits mentioned
- Compensation and benefits
- - Base salary: USD 150,000 to 300,000 per year
- - Equity
- - Health, dental and vision coverage
- - Retirement plan with employer match
- - Unlimited paid time off
- Location and work model
- - San Francisco, CA, United States or New York, NY, United States
- - On-site, 5 days per week in office
- - Full-time
Requirements mentioned
- What we're looking for
- - 3 to 10 years of experience in infrastructure or platform engineering.
- - Background at a company known for a demanding engineering standard, ideally on an early team.
- - Deep hands-on knowledge of a leading public cloud, plus running containerized workloads on Kubernetes at scale.
- - A profile that fits either a cloud infrastructure engineer working across providers or a data infrastructure engineer building complex ingestion pipelines.
- - A history of quick growth in title or scope.
- - Working familiarity with data pipeline tools and real-time processing, with cloud infrastructure as the primary strength.
- - A careful, security-first mindset and practical exposure to data governance.
- - Comfort with observability, logging and monitoring tooling.
- - Awareness of common regulatory and audit frameworks.
- - A degree from a top 25 Computer Science program.
- - Genuine motivation to join an early-stage startup and the ability to move quickly while building sturdy systems.
- - Candidates based in New York generally need 4 to 5+ years of experience, unless they have strong startup experience or a top 25 Computer Science degree.
- Bonus points
- - Exposure to machine learning infrastructure.
- - Experience in regulated industries.
- - Experience with managed AI platforms.
- - Knowledge of financial services regulations.
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