Job Description

Who is Recruiting from Scratch
Recruiting from Scratch is a premier talent firm that focuses on placing the best product managers, software, and hardware talent at innovative companies. Our team is 100% remote and we work with teams across the United States to help them hire.

Title of Role: Senior Software Engineer, Backend (Infrastructure)
Location: San Francisco, CA (FiDi)
Company Stage of Funding: Early-stage (recently raised ~$30M+, Series A upcoming)
Office Type: On-site (5 days/week)
Salary: $250,000 – $300,000 + Competitive Equity + Bonus (profit share)

Our client is a rapidly growing AI infrastructure company building the backbone for next-generation agentic systems and data workflows. They are working at the frontier of AI by enabling large-scale data generation, evaluation pipelines, and human-in-the-loop systems that power advanced machine learning models.

The company is already generating significant revenue with strong growth and is collaborating with leading AI labs and high-growth startups. With a highly technical and talent-dense team, they are tackling some of the most complex infrastructure challenges in AI today.

Operating across domains like healthcare and finance, they are building scalable systems to support massive data throughput and real-time processing—positioning themselves as a key infrastructure layer in the evolving AI ecosystem.

What You Will Do

• Design and build core infrastructure powering data generation, evaluation pipelines, and AI systems
• Own scalability, reliability, and performance of foundational backend systems
• Architect distributed systems to handle high-throughput, large-scale datasets
• Collaborate with product and vertical engineering teams to enable rapid development
• Establish best practices for system design, observability, and deployment
• Improve infrastructure for real-time and batch processing workloads
• Mentor junior engineers and contribute to a high-performance engineering culture
• Work across the stack when needed (backend-heavy with some full-stack exposure)
• Take ownership of critical systems from design through production

Ideal Candidate Background

• 5–8 years of software engineering experience
• Strong backend and infrastructure experience (distributed systems, scalability, reliability)
• Experience building and operating production systems in cloud environments (AWS or GCP)
• Proficiency in languages such as Python or Node.js
• Experience with modern infrastructure tools (Kubernetes, Kafka, Redis, etc.)
• Strong understanding of system design, data architecture, and high-throughput systems
• Experience working in startup or high-growth environments
• Ability to operate independently and take ownership of critical systems

Preferred

• Experience working on data infrastructure, ML pipelines, or human-in-the-loop systems
• Background at high-growth startups, data platforms, or quantitative environments
• Experience with real-time systems, event pipelines, or streaming architectures
• Familiarity with Elasticsearch or large-scale search/indexing systems
• Strong academic background in computer science or related field
• Experience mentoring engineers or acting as a technical leader

Compensation and Benefits

• Competitive base salary: $250,000 – $300,000
• Competitive equity package
• Performance-based bonus (profit share upside)
• Opportunity to work on cutting-edge AI infrastructure
• High ownership and impact as an early engineering hire
• Work alongside a highly technical and talent-dense team
• Fast-growing company with strong revenue traction

Why Join

This is a rare opportunity to work on the foundational infrastructure powering the next generation of AI systems. You’ll tackle deeply technical challenges around scalability, distributed systems, and real-time data processing—at a company already operating at meaningful scale.

If you’re an engineer who enjoys building core systems, solving hard infrastructure problems, and operating in a high-performance startup environment, this role offers significant ownership, technical depth, and upside.