AI Backend Engineer Jobs

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Design, build, and own backend systems end-to-end, including services, APIs, data pipelines, and infrastructure that power products. Solve complex technical challenges across distributed systems, scaling, concurrency, and performance. Integrate and operate large generative AI models in production, including deploying, serving, and scaling systems combining internal research and external capabilities. Instrument, experiment, and iterate in production to continuously improve system and product quality. Design and operate core platform infrastructure, including integrations with third-party providers, storage systems, security, and internal APIs.

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The Senior Software Engineer will contribute to building a managed platform for the entire application development lifecycle with a focus on leveraging Machine Learning models including Large Language Models (LLMs). Responsibilities include implementing and maintaining systems for fine-tuning large foundation models such as SFT, PEFT, LoRA, and adapters, including multi-node orchestration, checkpointing, failure recovery, and cost-efficient scaling. They will implement and maintain end-to-end training pipelines for Large Language Models and components for distillation and reinforcement learning pipelines like preference optimization, policy optimization, and reward modeling. They will develop and maintain core agent execution infrastructure and implement features for dataset, model, and experiment management focusing on versioning, lineage, evaluation, and reproducible fine-tuning. The role involves working closely with Senior and Principal Engineers as well as product and platform teams to implement system abstractions and APIs and contributing to technical discussions on training runtimes, scheduling, storage, and model lifecycle management. Additionally, the role requires engaging with the open-source LLM ecosystem and involves significant implementation ownership for core system components.

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As a Senior Software Engineer working closely with the Engineering Manager for Transcription and Note Generation, you will develop AI-powered solutions that transform clinical conversations into structured medical documentation. Your responsibilities include designing and developing robust, scalable systems for real-time processing and examination of clinical conversations, collaborating with the AI/ML team to integrate and optimize medical speech transcription and note generation, designing, building, and maintaining APIs and services for transcription and note generation capabilities, implementing systems to ensure high accuracy and reliability in medical documentation generation, optimizing performance for real-time results in clinical settings, and contributing to the overall architecture and best practices for scalable and maintainable systems.

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The AI Safety Engineer is responsible for designing and building scalable backend infrastructure for content moderation, abuse detection, and agents guardrails by deploying AI/ML models into production systems. They will architect robust APIs, data pipelines, and service architectures to support real-time and batch moderation workflows. The role includes implementing comprehensive monitoring, alerting, and observability systems, establishing SLIs, SLOs, and performance benchmarks. The engineer will collaborate with ML engineers to translate research models into production-ready systems and integrate them across the product suite. Additionally, they will drive technical decisions and contribute to the vision for the safety roadmap to build next-generation platform guardrails for scale and precision.

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Design and build extensible backend systems that support flexible configurations for different customers and content types. Develop infrastructure that interfaces cleanly with large language models (LLMs), enabling prompt engineering, context injection, and modular evaluation workflows. Build tooling and platforms that enable fast iteration by AI engineers and analysts, including declarative pipelines, parameterized jobs, and reproducible experiments. Prioritize ease of deployment, integration, and testing, both for internal teams and external partners. Collaborate closely with product, data, and policy teams to translate nuanced safety needs into scalable, maintainable software systems.

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The Software Engineer is responsible for utilizing the Agent Development Kit (ADK) to design, develop, and deploy autonomous agents and "skills" capable of multi-step data retrieval tasks. They design and develop backend systems and APIs to expose bioinformatics data and implement advanced search and retrieval mechanisms to provide LLMs with up-to-date grounded information. Their duties include tuning storage technologies, creating high-performance query plans, designing solutions, and adapting existing approaches to solve issues within web app architecture and interfaces. They operationalize production-grade data pipelines using processing engines like Apache Beam, collaborate with other engineers to address document extraction, enrichment, and retrieval challenges, and model scientific experiments from unstructured data. The engineer also troubleshoots and resolves production issues promptly, ensures code is testable, self-documenting, and reliable, communicates decisions to impacted teams, works on client-facing projects with large pharmaceutical companies, and balances independent work with collaborative efforts for complex architectural changes.

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The Engineering Manager for the Engine and Platform leads the team responsible for building, maintaining, and deploying the runtime for customers to run, manage, secure, and understand AI tools, enabling advanced agentic use-cases. This role involves scaling the team owning the development of the platform and services, which includes distributed systems engineers and authorization/identity experts developing features like MCP gateways, roles and permissions, and platform-as-service capabilities for tool executions. The manager ensures the team is unblocked, aligns the team's work with the product organization, and stays technically engaged through code reviews, critical contributions, and occasional hands-on coding. Responsibilities include owning deliverables, stability, and uptime, shaping product vision and architecture, owning technical direction and prioritization, hiring and mentoring engineers, defining and delivering platform features, and ensuring reliability, security, and enterprise readiness. The manager also focuses on building leverage into systems through automation and agents to improve efficiency and is expected to navigate ambiguity and evolving standards in AI tools.

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Engineers at Giga work on problems like building AI agents with almost no hallucination rates, creating a voice experience that is better than talking to humans, and creating self-learning agents that optimize metrics.

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Ship production-grade backend and frontend features for core member and provider flows using React, TypeScript, APIs, and data layers, ensuring high polish and reliability. Own features end-to-end, including specification, building, testing, deployment, monitoring, and handling complex state, permissions, and edge cases. Build and maintain robust system hygiene, including instrumentation, dashboards and alerts, CI/CD pipelines, code reviews, and production debugging. Design, implement, and maintain AI-powered workflows comprising tool/function calling, structured outputs, Retrieval-Augmented Generation (RAG), evals, tracing, observability, prompt versioning, and guardrails. Build and operate workflow and agent flows using orchestration patterns similar to Temporal, Dagster, or Airflow, managing retries, idempotency, asynchronous job queues, and failure handling. Collaborate closely with cross-functional partners to deliver reliable, scalable, and user-centric healthcare products.

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Design, develop, and maintain web applications and backend services that integrate ML-powered features. Collaborate closely with Machine Learning Engineers and Product Managers to understand ML system requirements and translate them into robust software solutions. Build reliable, scalable, and low-latency services that support ML inference, data workflows, and AI-driven user experiences. Use LLMs to build scalable and reliable AI agents. Own the full software development lifecycle: design, implementation, testing, deployment, monitoring, and maintenance. Ensure high standards for code quality, testing, observability, and operational excellence. Troubleshoot production issues and participate in on-call or support rotations when needed. Mentor junior engineers and contribute to technical best practices across teams. Act as a strong cross-functional partner between product, engineering, and ML teams.