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.

Software Engineer – Core Systems

Location: San Francisco, CA

Company Stage of Funding: Early Stage / VC-Backed

Office Type: On-site — 5 days per week

Salary: $215,000 – $275,000 Base

Equity: Competitive Equity

Visa: Open to Visa Transfers and Visa Sponsorships, including OPT, H-1B transfers, new H-1B, and TN

Experience: 6+ years of software engineering experience; 5+ years in data infrastructure or backend distributed systems

Employment Type: Full-time

Hiring Count: 1 candidate

Company Description

Our client is an early-stage, VC-backed healthcare technology company building AI-powered infrastructure to improve medical imaging and diagnostic workflows.

The company is developing a healthcare platform that combines medical imaging, software infrastructure, and AI to help healthcare providers address significant capacity and workflow challenges.

The engineering team is small and highly autonomous, giving engineers substantial ownership over foundational infrastructure. The platform handles large-scale medical imaging data and connects with existing healthcare systems and workflows.

The engineering challenges span data ingestion, data warehousing, distributed systems, model inference, cloud infrastructure, deployment automation, observability, security, and healthcare integrations.

This is an opportunity for a senior engineer to own core backend infrastructure end-to-end and work closely with ML engineers and other technical leaders to build reliable systems in a highly regulated environment.

What You Will Do

Build Core Backend & Data Infrastructure

• Design, build, and operate core backend infrastructure end-to-end.
• Build scalable data ingestion pipelines and ETL systems for large volumes of medical imaging data.
• Design and maintain data warehousing infrastructure supporting analytics and machine learning workloads.
• Build backend services and distributed systems running on AWS.
• Design systems that can reliably process, store, and distribute large datasets.
• Continuously improve architecture, scalability, performance, and maintainability.

Build Distributed Inference & ML Infrastructure

• Collaborate closely with ML engineers to support production model inference workflows.
• Build infrastructure for distributed model inference and AI-powered processing pipelines.
• Design reliable systems for moving data between ingestion, processing, storage, and inference layers.
• Support evolving ML infrastructure requirements as models and workloads scale.
• Help bridge the gap between backend engineering and machine learning infrastructure.

Own Reliability, Observability & Infrastructure

• Own reliability and operational performance across the backend stack.
• Build monitoring, logging, alerting, and debugging systems.
• Identify and resolve production issues across distributed services and data pipelines.
• Lead capacity planning and infrastructure improvements as system usage grows.
• Build CI/CD and deployment automation that enables a lean engineering team to ship safely and quickly.
• Take responsibility for systems in production without relying on a separate SRE organization.

Build Secure & Compliant Healthcare Systems

• Design systems handling protected health information (PHI) with security and compliance built in from the beginning.
• Implement appropriate encryption, access controls, authentication, authorization, and audit logging.
• Build infrastructure that supports HIPAA-compliant data handling and operational practices.
• Work closely with engineering and product stakeholders to identify and mitigate infrastructure and security risks.
• Design reliable integrations with healthcare systems and data workflows as the platform expands.

Ideal Candidate Background

Experience Requirements

• 6+ years of professional software engineering experience.
• 5+ years preferred in data infrastructure, backend engineering, or distributed systems.
• Strong experience building and operating production backend infrastructure.
• Experience working with distributed systems and high-volume data pipelines.
• Experience owning systems end-to-end in production.
• Demonstrated ability to operate autonomously without heavy management.
• Experience working in strong engineering environments with high standards for infrastructure quality.
• Experience at a startup, high-growth technology company, or similarly high-ownership engineering environment is strongly preferred.
• Comfortable working as part of a small engineering team where engineers own broad areas of the stack.

Technical Requirements

• Strong production experience with Python and/or Go.
• Strong backend engineering fundamentals.
• Experience designing distributed systems.
• Experience building data pipelines and ETL infrastructure.
• Experience with data warehousing systems.
• Strong AWS experience.
• Experience with Docker and modern deployment infrastructure.
• Experience building CI/CD pipelines and deployment automation.
• Strong understanding of production reliability and infrastructure operations.
• Experience with observability, logging, monitoring, and debugging.
• Experience with relational and/or analytical data systems.
• TypeScript experience is a plus.
• React experience is a plus.

Data, Infrastructure & Systems Requirements

• Strong understanding of distributed systems architecture.
• Experience designing reliable data ingestion pipelines.
• Experience processing large datasets or high-throughput workloads.
• Experience with asynchronous processing and distributed workloads.
• Understanding of system reliability, fault tolerance, and failure recovery.
• Experience with capacity planning and infrastructure scaling.
• Experience supporting machine learning infrastructure or model inference is strongly preferred.
• Experience working with cloud-based infrastructure at production scale.
• Understanding of ETL/ELT patterns and data lifecycle management.
• Experience integrating multiple backend services and infrastructure components.

Security & Healthcare Requirements

• Experience designing secure production systems.
• Understanding of encryption, authentication, authorization, and access controls.
• Experience working with sensitive or regulated data is strongly preferred.
• Familiarity with HIPAA or other healthcare security/compliance requirements is a plus.
• Experience integrating with healthcare systems or medical data is a plus.
• Familiarity with healthcare interoperability standards such as DICOM or HL7 is a plus.

Soft Skills

• Highly autonomous and comfortable operating without heavy management.
• Strong ownership mentality.
• Excellent debugging and problem-solving skills.
• Strong engineering judgment and attention to infrastructure quality.
• Comfortable working across backend, data, infrastructure, and ML systems.
• Able to prioritize effectively in a small, fast-moving team.
• Strong communication and collaboration skills.
• Comfortable working through ambiguous technical problems.
• Pragmatic and focused on building reliable systems rather than over-engineering.
• Comfortable taking responsibility for systems after they reach production.

Compensation & Benefits

• $215,000 – $275,000 base salary, depending on experience.
• Competitive equity.
• Full-time employment.
• On-site work in San Francisco, 5 days per week.
• Visa transfers supported, including OPT and H-1B transfers.
• Visa sponsorship supported, including new H-1B and TN where applicable.
• Opportunity to work directly on foundational infrastructure at an early-stage healthcare technology company.
• Significant technical ownership within a small engineering team.

Why Join

• Own the core infrastructure powering an AI-driven healthcare platform.
• Build production data pipelines, distributed systems, and inference infrastructure from the ground up.
• Work with a small team where engineers have broad ownership and direct technical influence.
• Solve challenging infrastructure problems involving large-scale medical imaging and AI workloads.
• Work directly with ML engineers to build production-grade model infrastructure.
• Own reliability, observability, deployment, and infrastructure rather than working within a separate SRE organization.
• Build secure systems handling highly sensitive healthcare data.
• Work at the intersection of backend engineering, distributed systems, data infrastructure, cloud infrastructure, and AI.
• Have the opportunity to shape foundational architecture at an early-stage company.