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.

Senior Software Engineer

Location

San Francisco, California

Work Arrangement: Hybrid — 3 days in-office per week

Position: Full-time

Company Stage: Profitable, fast-growing technology company

Team Size: 250+ engineers

Experience: 5–8 years

Compensation: $170,000–$311,000 base + 15% target bonus + competitive equity

Visa: Open to eligible visa transfers, including OPT and H-1B transfers. New sponsorship is not specified.

Company Description

We are building an AI-powered, end-to-end go-to-market platform that helps sales, marketing, and revenue teams find prospects, engage customers, and close deals more effectively.

The platform combines a large B2B database with AI-native tools for prospecting, customer engagement, sequencing, and deal intelligence. More than 500,000 companies rely on the platform, which is currently being rebuilt around AI and agent-driven workflows.

The company is profitable, growing quickly, and investing heavily in AI-native product development. The engineering organization operates at significant scale, with a strong emphasis on ownership, continuous improvement, and bold experimentation.

Engineers are encouraged to use AI-assisted development tools as a core part of their workflow, applying them to design, investigation, implementation, and problem-solving.

What You Will Do

• Own systems end-to-end: Design, build, deploy, and operate significant systems and features, taking accountability for their performance, reliability, correctness, and cost.
• Build distributed systems at scale: Develop and operate systems supporting hundreds of thousands of companies, selecting and combining the right technologies for complex engineering problems.
• Solve ambiguous technical challenges: Break down difficult problems, make sound architectural decisions, and drive solutions through to production.
• Build AI-native agent systems: Develop AI-powered capabilities and agent-driven workflows used by customers around the world.
• Make thoughtful technology decisions: Work with technologies such as Kafka, databases, and distributed infrastructure, choosing the right tools for each problem rather than relying on a single stack.
• Collaborate across teams: Partner with engineering, product, design, and leadership to deliver high-impact features and solve cross-functional challenges.
• Use AI-assisted development: Integrate AI coding tools and agents into your daily workflow to accelerate design, investigation, and implementation.
• Improve production systems: Continuously improve system architecture, scalability, reliability, and performance as the product evolves.
• Take significant ownership: Work independently on important engineering initiatives while collaborating with a high-bar engineering team.

Ideal Candidate Background

Required Experience

• 5–8 years of professional experience in backend or full-stack software engineering.
• Demonstrated experience building and operating large-scale distributed systems.
• Strong software engineering fundamentals and the ability to make sound architectural decisions.
• Experience owning significant systems or features from design through production and ongoing operation.
• Ability to select and combine technologies to solve complex engineering problems.
• Strong problem-solving skills and comfort working through ambiguous requirements.
• A track record of delivering reliable, performant, and maintainable production software.
• Experience working collaboratively across engineering and product teams.
• Interest in building AI-native products and agent-driven systems.
• Willingness to work in a hybrid arrangement, with three days per week in the San Francisco office.

Technical Skills

Core Engineering

• Backend or full-stack software engineering.
• Distributed systems design and implementation.
• Production system ownership, reliability, scalability, and performance.
• Technology selection and architectural trade-offs.

Relevant Technologies

• Languages and frameworks: Ruby, Ruby on Rails, Python, React.
• Databases and search: MongoDB, Elasticsearch.
• Infrastructure and deployment: Kubernetes, Docker, Terraform.
• Cloud: Google Cloud Platform (GCP).
• Distributed systems: Experience with Kafka, databases, and comparable technologies is relevant.

Candidates do not need to have worked with every technology listed. Prioritize demonstrated distributed systems expertise, strong engineering judgment, and the ability to learn and combine technologies effectively.

Preferred Background

• Experience operating distributed systems at significant scale.
• Experience building backend platforms or complex full-stack products.
• Experience with event-driven architecture, messaging systems, or Kafka.
• Experience with databases, search infrastructure, and data-intensive applications.
• Experience deploying and operating services using containers and cloud infrastructure.
• Experience with AI-powered applications, agentic systems, or AI-native product development.
• Experience using AI coding assistants or agents as part of everyday engineering workflows.
• Experience taking ownership of complex systems with clear accountability for reliability, performance, and cost.

Working Style and Culture

• Extreme ownership: Takes responsibility for systems from initial design through production operation.
• Strong engineering judgment: Knows when and how to use different technologies to solve a problem.
• High technical standards: Cares about reliability, performance, correctness, scalability, and cost.
• Comfort with ambiguity: Can break down complex problems and drive them toward practical solutions.
• AI-native mindset: Uses AI-assisted development tools to increase engineering leverage and move faster.
• Collaboration: Works effectively with product, design, engineering, and leadership.
• Continuous improvement: Looks for ways to improve systems, workflows, and technical decision-making.
• Bias toward execution: Moves from investigation and design to shipped outcomes.
• Adaptability: Can contribute across high-priority projects as business needs evolve.

Compensation and Benefits

• Base salary: $170,000–$311,000 for San Francisco.
• Target bonus: 15% of base salary.
• Equity: Competitive equity.
• Work arrangement: Hybrid, with three days per week in the San Francisco office.
• AI development tools: Budget and autonomy to use tools such as Copilot and Cursor.
• Ownership and impact: Opportunity to own significant systems and work on high-priority engineering initiatives.
• Leadership exposure: Opportunity for senior individual contributors to collaborate with senior engineering leaders and executives.

Compensation within the band depends on leveling during the interview process, based on experience and performance. Candidates targeting the top of the range must demonstrate that they would perform among the top 10% of engineers at their level.

Why Join?

• Build AI-native products used by more than 500,000 companies.
• Work on large-scale distributed systems supporting a widely used go-to-market platform.
• Help rebuild an established, profitable product around AI and agent-driven workflows.
• Own meaningful systems from architecture and implementation through production operation.
• Work in an engineering culture that values strong technical judgment and end-to-end accountability.
• Use AI-assisted development tools to improve speed and engineering productivity.
• Collaborate with experienced engineers and senior technical leadership.
• Receive competitive compensation, a target bonus, and equity.