Job description

We're seeking a versatile, technically strong Backend Software Engineer to design, build, and own backend infrastructure for imaging engineering and quality workflows across Camera, Photos, and Image Quality, building and operating Python REST API services, designing data models for enormous volumes of image and metadata records, and running and scaling asynchronous compute jobs, including the serving infrastructure for our AI/ML models. The ideal candidate has a solid grasp of distributed-systems fundamentals and is comfortable owning a service from API design through production operation, writing code with an eye toward maintainability, correctness, and long-term operability, and is equally at home designing a new service, debugging a tricky async job, standing up model-serving infrastructure, or sitting with a partner team to understand what they actually need. You hold AI-powered features to the same engineering standards as any other production code, and you treat cross-functional communication as a core part of the job. BS in Computer Science, Computer Engineering, or equivalent experience. 4+ years of professional software engineering experience shipping production backend systems. Strong proficiency in Python, with a track record of owning production backend services end to end. Strong understanding of REST API design and experience building and operating production REST services at scale. Demonstrated experience hosting and serving AI/ML models (LLMs, vision models, or similar) in production, including infrastructure for inference, scaling, and monitoring, not just integrating third-party hosted APIs. Working knowledge of asynchronous job execution patterns (background workers, task queues, or similar) for long-running computations, and experience scaling these systems under load. Solid understanding of distributed-systems fundamentals: consistency, coordination, failure handling, and tradeoffs between them. Solid understanding of software engineering fundamentals: data modeling, API design, testing, debugging, and code review. Strong written and verbal communication skills, with a demonstrated ability to work effectively with partners outside of engineering. Hands-on experience with specific self-hosted GPU inference frameworks (e.g., vLLM, Triton, Ray Serve, or similar) at production scale, beyond the general hosting/serving experience required above. Experience building production features with LLM APIs (e.g., OpenAI, Anthropic, or on-device models), including prompt design, context window management, output validation, and graceful degradation. Familiarity with multimodal or computer vision models applied to image analysis, quality assessment, or visual data retrieval, with an understanding of where these models succeed and fail in practice. Experience with vector databases or semantic search (e.g., pgvector, Pinecone, Weaviate) for unstructured or high-dimensional data retrieval pipelines. Understanding of MLOps principles: model deployment pipelines, versioning strategies, evaluation frameworks, A/B testing for AI features, and production monitoring for model quality and cost. Awareness of bias and fairness considerations in AI systems, particularly in visual domains, including diverse evaluation datasets, inclusive quality benchmarks, and responsible deployment practices. Hands-on operational experience with container orchestration (e.g., Kubernetes) and infrastructure-as-code for distributed systems, beyond the conceptual fundamentals required above. Familiarity with Solr (or other search platforms such as Elasticsearch) for indexing and querying large datasets. Familiarity with Redis, whether as a cache, message broker, or coordination primitive. Comfort working with image data, metadata pipelines, or scientific/engineering workflows. Comfortable and adaptable in a fast-paced environment with shifting priorities and multiple stakeholders.

Requirements

Must-haves

BS in Computer Science, Computer Engineering, or equivalent experience.
4+ years of professional software engineering experience shipping production backend systems.
Strong proficiency in Python, with a track record of owning production backend services end to end.
Strong understanding of REST API design and experience building and operating production REST services at scale.
Demonstrated experience hosting and serving AI/ML models (LLMs, vision models, or similar) in production, including infrastructure for inference, scaling, and monitoring, not just integrating third-party hosted APIs.
Working knowledge of asynchronous job execution patterns (background workers, task queues, or similar) for long-running computations, and experience scaling these systems under load.
Solid understanding of distributed-systems fundamentals: consistency, coordination, failure handling, and tradeoffs between them.
Solid understanding of software engineering fundamentals: data modeling, API design, testing, debugging, and code review.
Strong written and verbal communication skills, with a demonstrated ability to work effectively with partners outside of engineering.
Hands-on experience with specific self-hosted GPU inference frameworks (e.g., vLLM, Triton, Ray Serve, or similar) at production scale, beyond the general hosting/serving experience required above.
Experience building production features with LLM APIs (e.g., OpenAI, Anthropic, or on-device models), including prompt design, context window management, output validation, and graceful degradation.
Familiarity with multimodal or computer vision models applied to image analysis, quality assessment, or visual data retrieval, with an understanding of where these models succeed and fail in practice.
Experience with vector databases or semantic search (e.g., pgvector, Pinecone, Weaviate) for unstructured or high-dimensional data retrieval pipelines.
Understanding of MLOps principles: model deployment pipelines, versioning strategies, evaluation frameworks, A/B testing for AI features, and production monitoring for model quality and cost.
Awareness of bias and fairness considerations in AI systems, particularly in visual domains, including diverse evaluation datasets, inclusive quality benchmarks, and responsible deployment practices.
Hands-on operational experience with container orchestration (e.g., Kubernetes) and infrastructure-as-code for distributed systems, beyond the conceptual fundamentals required above.
Familiarity with Solr (or other search platforms such as Elasticsearch) for indexing and querying large datasets.
Familiarity with Redis, whether as a cache, message broker, or coordination primitive.
Comfort working with image data, metadata pipelines, or scientific/engineering workflows.
Comfortable and adaptable in a fast-paced environment with shifting priorities and multiple stakeholders.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

pythonrestapiaimlllmkubernetes

Platform: apple
Effort: medium

Benefits

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Vision