Pear VC
Founding Infra / Platform Engineer - Known
Pear VC, San Francisco, California, United States, 94199
About the Role
You'll be the foundational engineer owning Known's
core infrastructure and platform systems
- the backbone that powers our AI-driven matching, voice, and scheduling experiences. From cloud infrastructure and data orchestration to performance monitoring and model deployment assistance, you'll design and scale the systems that make Known fast, reliable, and secure.
You'll work directly with the founding team (AI/ML, product, and design) to establish Known's
technical foundation
- shaping not just our architecture, but our engineering culture and best practices from day one. This role is ideal for a pragmatic builder who enjoys going from "blank slate" to production and thrives in early-stage environments where reliability, velocity, and simplicity matter most.
Responsibilities Design and manage
cloud infrastructure (AWS-first, with IaC via Terraform). Establish CI/CD pipelines
and best practices for rapid, safe iteration (GitHub Actions, Docker, Kubernetes, etc.). Build and maintain
scalable data ingestion and orchestration pipelines to support ML and product analytics. Administer and optimize
our databases - PostgreSQL (with pgvector for embeddings) and analytical warehouse. Collaborate with AI/ML engineers
to deploy and monitor LLM and matching models for inference, evaluation, and retraining. Implement observability
(logging, metrics, traces, alerts) across backend services, data jobs, and model endpoints. Drive reliability and scalability
across our web, mobile, and agentic systems - from real-time voice matching to background batch workflows. Collaborate cross-functionally
with product, design, and ML teams to ensure infrastructure aligns with user and business needs. Requirements
4+ years of experience in
infrastructure, platform, or data engineering
(startup or high-growth environments preferred). Strong proficiency in
Python ,
TypeScript , and scripting (Bash/YAML). Deep understanding of
cloud architecture
(AWS, GCP, or similar) and
Infrastructure-as-Code
(Terraform, Pulumi, or CloudFormation). Solid experience with
containerization and orchestration
(Docker, Kubernetes, ECS). Proven ability to design and operate
data pipelines
and distributed systems. Experience with
PostgreSQL
(ideally with pgvector or embeddings),
data modeling , and
schema design
for real-time and analytical workloads. Familiarity with
ML/AI workflows
(model training, inference, monitoring) and
feature stores
is a plus. DevOps fundamentals: observability, cost optimization, and security. Collaborative mindset, strong ownership, and bias toward shipping working systems fast. Example Projects
Stand up a
data lake + warehouse
for storing and analyzing user signals, transcripts, and model outputs. Build
real-time ingestion
from app, agent, and third-party APIs (OpenAI, Twilio, Stripe). Deploy and scale
voice agent infrastructure
with low-latency streaming, recording, and monitoring. Design the
CI/CD and observability stack
for Known's core services. Assist ML engineers in Implementing
model deployment pipelines
for embedding generation and re-ranking inference.
Why This Role
You'll help define the technical foundation of a product that blends
human connection and advanced AI . As one of Known's first engineers, you'll make decisions that influence how the product scales, performs, and evolves - from the data stack to the deployment layer. If you're excited by the idea of shaping the platform behind a category-defining AI product, this is the place to build it.
You'll be the foundational engineer owning Known's
core infrastructure and platform systems
- the backbone that powers our AI-driven matching, voice, and scheduling experiences. From cloud infrastructure and data orchestration to performance monitoring and model deployment assistance, you'll design and scale the systems that make Known fast, reliable, and secure.
You'll work directly with the founding team (AI/ML, product, and design) to establish Known's
technical foundation
- shaping not just our architecture, but our engineering culture and best practices from day one. This role is ideal for a pragmatic builder who enjoys going from "blank slate" to production and thrives in early-stage environments where reliability, velocity, and simplicity matter most.
Responsibilities Design and manage
cloud infrastructure (AWS-first, with IaC via Terraform). Establish CI/CD pipelines
and best practices for rapid, safe iteration (GitHub Actions, Docker, Kubernetes, etc.). Build and maintain
scalable data ingestion and orchestration pipelines to support ML and product analytics. Administer and optimize
our databases - PostgreSQL (with pgvector for embeddings) and analytical warehouse. Collaborate with AI/ML engineers
to deploy and monitor LLM and matching models for inference, evaluation, and retraining. Implement observability
(logging, metrics, traces, alerts) across backend services, data jobs, and model endpoints. Drive reliability and scalability
across our web, mobile, and agentic systems - from real-time voice matching to background batch workflows. Collaborate cross-functionally
with product, design, and ML teams to ensure infrastructure aligns with user and business needs. Requirements
4+ years of experience in
infrastructure, platform, or data engineering
(startup or high-growth environments preferred). Strong proficiency in
Python ,
TypeScript , and scripting (Bash/YAML). Deep understanding of
cloud architecture
(AWS, GCP, or similar) and
Infrastructure-as-Code
(Terraform, Pulumi, or CloudFormation). Solid experience with
containerization and orchestration
(Docker, Kubernetes, ECS). Proven ability to design and operate
data pipelines
and distributed systems. Experience with
PostgreSQL
(ideally with pgvector or embeddings),
data modeling , and
schema design
for real-time and analytical workloads. Familiarity with
ML/AI workflows
(model training, inference, monitoring) and
feature stores
is a plus. DevOps fundamentals: observability, cost optimization, and security. Collaborative mindset, strong ownership, and bias toward shipping working systems fast. Example Projects
Stand up a
data lake + warehouse
for storing and analyzing user signals, transcripts, and model outputs. Build
real-time ingestion
from app, agent, and third-party APIs (OpenAI, Twilio, Stripe). Deploy and scale
voice agent infrastructure
with low-latency streaming, recording, and monitoring. Design the
CI/CD and observability stack
for Known's core services. Assist ML engineers in Implementing
model deployment pipelines
for embedding generation and re-ranking inference.
Why This Role
You'll help define the technical foundation of a product that blends
human connection and advanced AI . As one of Known's first engineers, you'll make decisions that influence how the product scales, performs, and evolves - from the data stack to the deployment layer. If you're excited by the idea of shaping the platform behind a category-defining AI product, this is the place to build it.