Senior Software Engineer, Developer Productivity at Lambda

San Francisco, California, United States

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$266,000 – $445,000Compensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

AI, Cloud Computing, Machine LearningIndustries

Requirements

5+ years total software engineering experience with 2+ years building developer platforms, CI/CD systems, or infrastructure tooling
Strong programming experience in Python and/or Go, with expertise in system-level debugging, testing, and building maintainable APIs
Demonstrated experience designing and operating production services at scale, with focus on reliability, observability, and developer experience
Deep knowledge of CI/CD systems (GitHub Actions, ArgoCD), containerization (Kubernetes), and infrastructure-as-code (Terraform)
Proven ability to establish technical standards, drive adoption across engineering teams, and mentor other engineers
Excellent written and verbal communication skills, with experience facilitating working groups and building consensus on technical decisions

Responsibilities

Own design, development, and operation of the shared foundations platform, ensuring high availability, developer productivity, and maintainable architecture
Work with engineering teams to identify opportunities to refactor common needs into the shared foundation
Incorporate reusable service reliability practices like retries, circuit breakers, and observability instrumentation
Deliver technical leadership on CI/CD workflows, documentation systems, API standards, and operational practices across engineering teams
Build and maintain reusable GitHub Actions workflows, shared libraries, and developer tooling that accelerate delivery
Establish and steward documentation standards, information architecture, and freshness policies to ensure engineering docs remain discoverable and up-to-date
Partner with infrastructure, product engineering, and DevOps teams on roadmap priorities, technical tradeoffs, and adoption strategies
Drive API versioning, contract-change management, and cross-team communication patterns to reduce integration friction
Lead working groups on CI/CD, Python/Go tooling, and design system standards, facilitating consensus and adoption
Mentor engineers, run design reviews, and help teams adopt bottom-up standards with clear exception processes
Monitor and improve KPIs including CI workflow adoption, docs freshness, time-to-green for new services, and PR-to-prod velocity

Skills

CI/CD

Software Engineering

API Standards

Developer Tooling

Service Reliability

Retries

Circuit Breakers

Platform Engineering

High Availability

Documentation Systems

Lambda

Cloud-based GPU services for AI training

About Lambda

Lambda Labs provides cloud-based services for artificial intelligence (AI) training and inference, focusing on large language models and generative AI. Their main product, the AI Developer Cloud, utilizes NVIDIA's GH200 Grace Hopper™ Superchip to deliver efficient and cost-effective GPU resources. Customers can access on-demand and reserved cloud GPUs, which are essential for processing large datasets quickly, with pricing starting at $1.99 per hour for NVIDIA H100 instances. Lambda Labs serves AI developers and companies needing extensive GPU deployments, offering competitive pricing and infrastructure ownership options through their Lambda Echelon service. Additionally, they provide Lambda Stack, a software solution that simplifies the installation and management of AI-related tools for over 50,000 machine learning teams. The goal of Lambda Labs is to support AI development by providing accessible and efficient cloud GPU services.

San Jose, CaliforniaHeadquarters

2012Year Founded

$372.6MTotal Funding

DEBTCompany Stage

AI & Machine LearningIndustries

201-500Employees

Risks

Nebius' holistic cloud platform challenges Lambda's market share in AI infrastructure.

AWS's 896-core instance may draw customers seeking high-performance cloud solutions.

Existential crisis in Hermes 3 model raises concerns about Lambda's AI model reliability.

Differentiation

Lambda offers cost-effective Inference API for AI model deployment without infrastructure maintenance.

Nvidia HGX H100 and Quantum-2 InfiniBand Clusters enhance Lambda's AI model training capabilities.

Lambda's Hermes 3 collaboration showcases advanced AI model development expertise.

Upsides

Inference API launch attracts enterprises seeking low-cost AI deployment solutions.

Nvidia HGX H100 clusters provide competitive edge in high-performance AI computing.

Strong AI cloud service growth indicates rising demand for Lambda's GPU offerings.

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