Senior Infrastructure Engineer, Applied AI

San Francisco CA

Fully onsite 5 days per week MondayFriday.

High-Growth Venture-Backed AI Infrastructure Company

On-site 5 days per week

$250000 $350000 Base

OTE: $300000 $450000

Competitive Equity

Open to visa transfers including OPT and H-1B transfers.

610 years of experience building and maintaining platform infrastructure distributed systems or large-scale production systems with continued full-stack engineering capabilities.

Full-time

Growth Hiring 68 hires

This is a high-growth AI infrastructure company building the data generation human-in-the-loop systems and evaluation infrastructure that powers leading AI teams and advanced AI applications. Its platform helps organizations generate manage and evaluate large-scale datasets used to develop and improve AI systems.

The company has experienced rapid growth and is expanding its infrastructure to support increasing data volumes demanding workloads and multiple product verticals. Its platform supports complex data-intensive workflows across areas such as healthcare finance and other enterprise applications.

As the company scales it needs a dedicated senior infrastructure engineer to own the foundational platform that enables engineering teams to build and ship products reliably. This person will help establish the architecture systems and engineering practices required to support high-throughput workloads distributed processing and continued company growth.

The engineering team is collaborative and fast-moving. You will work closely with vertical-focused engineers mentor junior team members and build shared infrastructure that multiple teams depend on. The role combines deep infrastructure and distributed systems expertise with enough full-stack capability to understand and contribute across the broader product.

This is a high-impact opportunity for an engineer who enjoys solving complex infrastructure problems taking ownership of critical systems and building scalable foundations in an early-stage high-growth environment.

• Design build and maintain the shared infrastructure powering data generation platforms human-in-the-loop systems and AI evaluation pipelines.
• Take ownership of foundational systems used across multiple product verticals.
• Architect scalable reliable services that support growing workloads and increasing data volumes.
• Build platform capabilities that enable other engineering teams to develop and deploy products efficiently.
• Identify infrastructure bottlenecks and design solutions that improve system performance scalability and maintainability.
• Establish clear architectural patterns and reusable infrastructure components.
• Make pragmatic decisions about system design performance cost and long-term reliability.

• Architect and develop distributed systems capable of processing large datasets and high-throughput workloads.
• Design services and workflows that remain reliable as traffic data volumes and system complexity increase.
• Work with Kubernetes and cloud environments including AWS and GCP.
• Build and maintain asynchronous processing systems and event-driven workflows.
• Use technologies such as Kafka Redis and Elasticsearch where appropriate to support data processing messaging caching and search.
• Diagnose performance issues reliability problems and complex production failures.
• Improve system resilience fault tolerance and operational efficiency.
• Ensure infrastructure can support the companys growing product and customer requirements.

• Establish engineering best practices for system design deployment monitoring and reliability.
• Build observability into core systems through appropriate logging metrics tracing and alerting.
• Define and improve reliability standards and operational practices for critical infrastructure.
• Identify failure modes and develop strategies for graceful degradation and recovery.
• Improve deployment workflows and infrastructure management practices.
• Partner with engineering teams to ensure platform services are dependable and easy to use.
• Help create consistent approaches to testing incident response documentation and production readiness.
• Balance rapid delivery with the reliability required for mission-critical high-throughput systems.

• Mentor junior engineers and help raise the teams technical standards.
• Collaborate with engineers across product verticals to understand infrastructure requirements.
• Translate team needs into reusable platform capabilities and reliable services.
• Lead technical discussions around architecture scalability performance and reliability.
• Provide guidance on code quality system design deployment practices and operational ownership.
• Remain hands-on with implementation while helping other engineers make effective technical decisions.
• Take initiative in identifying infrastructure gaps and driving solutions through production.
• Help build a collaborative high-performance engineering culture as the company grows.

• 610 years of professional software engineering experience.
• Strong experience building and maintaining production distributed systems or platform infrastructure.
• Experience designing and operating systems in cloud environments ideally AWS or GCP.
• Experience building infrastructure that supports large-scale data processing or high-throughput workloads.
• Experience working at a venture-backed startup ideally as an early engineer or founding engineer at a company that raised meaningful institutional funding.
• Alternatively experience at a company specializing in data infrastructure or large-scale data management.
• Experience taking ownership of foundational systems used by multiple engineering teams.
• Ability to mentor engineers while remaining hands-on as an individual contributor.
• Full-stack engineering capabilities with a clear strength in backend infrastructure and platform engineering.
• Comfort operating in a fast-moving environment where infrastructure needs evolve alongside the product.
• A bachelors degree in Computer Science or a closely related technical field is preferred with a strong academic background or equivalent demonstrated technical expertise.

• Strong experience designing building and operating distributed systems.
• Deep knowledge of platform infrastructure and production system architecture.
• Strong Kubernetes expertise.
• Experience with cloud infrastructure particularly AWS or GCP.
• Understanding of high-throughput data processing and scalable service design.
• Experience with infrastructure deployment production operations and system troubleshooting.
• Strong understanding of observability monitoring logging tracing and reliability engineering.
• Experience designing fault-tolerant services and resilient distributed workflows.
• Strong system design and architectural decision-making skills.
• Familiarity with infrastructure automation deployment practices and production incident management.
• Ability to reason about performance availability scalability and operational complexity.

• Experience building infrastructure for data-intensive or AI-related products is strongly preferred.
• Understanding of the infrastructure required to support data generation data processing and evaluation pipelines.
• Experience supporting human-in-the-loop workflows data annotation or data operations is valuable.
• Experience working with high-volume datasets and distributed processing workloads.
• Familiarity with Kafka or other event-streaming and messaging technologies.
• Experience with Redis Elasticsearch or comparable caching and search systems.
• Experience building platform services that support multiple product teams or business verticals.
• Ability to build reliable infrastructure that allows other engineers to move quickly without compromising system stability.
• Understanding of production observability system reliability and operational readiness.
• Strong interest in building foundational infrastructure for AI systems and data platforms.

• Backend- and infrastructure-oriented engineering background with the ability to contribute across the full stack when needed.
• Proficiency in or Python is preferred.
• Experience designing APIs backend services and reusable platform components.
• Ability to understand how infrastructure decisions affect product development and customer-facing systems.
• Experience collaborating with product-focused or vertical engineering teams.
• Ability to translate application requirements into reliable platform capabilities.
• Comfortable moving between architecture implementation debugging deployment and production support.
• Strong engineering judgment when balancing speed reliability scalability and maintainability.
• Ability to mentor others without moving entirely away from hands-on engineering.

• Strong ownership and accountability.
• Highly proactive and self-directed.
• Comfortable working in a high-growth startup environment.
• Strong technical judgment and problem-solving ability.
• Clear and direct communication.
• Collaborative working style and willingness to help other engineers.
• Ability to mentor junior engineers and share technical knowledge.
• Comfortable making decisions in ambiguous situations.
• Willingness to take responsibility for critical production systems.
• Strong attention to reliability and operational detail.
• Ability to prioritize high-impact infrastructure work.
• Practical execution-oriented mindset.
• Comfortable working closely with engineering leadership and multiple product teams.
• Willingness to remain hands-on while helping raise the teams engineering standards.

• $250000 $350000 base salary.
• Target OTE of $300000 $450000.
• Competitive equity.
• Open to visa transfers including OPT and H-1B transfers.
• Opportunity to own foundational infrastructure across a rapidly growing AI platform.
• Direct technical impact across multiple engineering teams and product verticals.
• Opportunity to mentor junior engineers and influence engineering practices.
• Fully onsite in San Francisco 5 days per week.

• Own and architect foundational infrastructure that multiple engineering teams depend on.
• Build distributed systems supporting large-scale data generation and AI evaluation workloads.
• Work on infrastructure challenges involving scalability reliability throughput and observability.
• Join a high-growth venture-backed AI company with significant engineering needs.
• Have substantial autonomy over core platform architecture and technical direction.
• Help establish engineering standards and operational practices as the company scales.
• Work closely with vertical-focused engineers across different product areas.
• Mentor junior engineers while continuing to contribute directly to production systems.
• Build reusable infrastructure that accelerates development across the company.
• Work on foundational systems with a direct impact on the companys ability to scale.