Backend Software Engineer - Web/ML Dev Ops

Chooch•San Mateo, CA

4h•Remote

About The Position

We’re looking for a Backend Software Engineer (US-based) to join our team in a full-time, remote role. You’ll build and own the backend systems that power our Computer Vision AI platform and analytics products—including APIs, data services, model-serving integrations, and the infrastructure that enables reliable deployments for healthcare, manufacturing, and retail customers. This role involves frequent collaboration with technical and non-technical stakeholders and close partnership with Solutions Engineering and ML teams. You’ll have significant opportunity to lead technical initiatives, improve platform reliability and scalability, and provide product feedback grounded in real-world deployments.

Requirements

Strong backend engineering experience building and maintaining production services and APIs.
Proficiency in Python (writing clean, testable, maintainable code).
Solid understanding of relational databases and SQL (preferably PostgreSQL), including schema design and query optimization.
Comfort operating in a Linux environment: command line, debugging, log analysis, and basic networking concepts.
Experience with modern software development practices: code reviews, testing, CI/CD, and shipping iteratively.
Proficiency with Git and collaborative workflows (feature branches, PRs, conflict resolution).
Strong problem-solving skills with a track record of diagnosing and resolving complex issues in production.
Clear communication skills and ability to collaborate with cross-functional partners (Solutions, ML, Product, customers).

Nice To Haves

Experience integrating or supporting ML/CV systems in production (model serving, inference pipelines, monitoring).
Working familiarity with computer vision / image processing libraries (e.g., OpenCV) to understand inputs/outputs and troubleshoot issues.
Experience with cloud platforms (AWS/Azure/GCP) and deploying scalable services.
Experience with containerization and orchestration (Docker, Kubernetes) and infrastructure best practices.
Exposure to frontend fundamentals (HTML/JS) enough to collaborate effectively with full-stack teammates.

Responsibilities

Backend Systems & API Development
Design, build, and maintain scalable backend services that support CV/LLM workflows, analytics, and customer-facing applications.
Develop and evolve REST/GraphQL APIs, internal service interfaces, and event-driven integrations.
Implement authentication/authorization patterns and ensure secure service-to-service communication.
Data Platforms & Analytics Enablement
Build and maintain the data layer (storage, indexing, retrieval) that supports analytics, reporting, and search across CV outputs.
Implement streaming and batch pipelines for ingesting camera/edge events and transforming model results into customer-ready insights.
Define data contracts, schemas, and quality checks to keep downstream systems sane (because data loves chaos).
Model Integration & Serving Collaboration
Partner with ML engineers to productionize model inference: deployment patterns, versioning, monitoring, and rollback strategies.
Integrate CV and LLM services into backend workflows with an emphasis on latency, throughput, and observability.
Support experimentation while ensuring production systems remain stable and maintainable.
Reliability, Performance, and Observability
Own service health via metrics, logs, tracing, alerting, and incident response practices.
Optimize system performance across API latency, queue backlogs, database efficiency, and inference throughput.
Improve platform robustness through testing strategy, CI/CD, and infrastructure-as-code collaboration.
Cross-Functional Delivery & Customer Impact
Work closely with Solutions Engineering to translate customer requirements into scalable backend solutions.
Troubleshoot complex issues in production deployments (you won’t be alone, but you will be the calm voice in the room).
Contribute to technical roadmaps and platform standards as we scale across customers and industries.
On-Site Travel and Field Testing (Occasional)
Some deployments are live with major Fortune 500 customers; occasional travel may be required to validate performance, gather requirements, and ensure systems operate reliably in real-world environments.