AI Software Engineer Jobs

View detail

Build end-to-end product features across frontend, backend, and AI integrations; design agent workflows that handle planning, tool use, failure, and recovery across multiple steps; integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions; design real-time AI interactions with streaming, partial results, and tight latency constraints; improve system reliability, observability, and fallback mechanisms; collaborate closely with ML, backend, and product teams to ship features end-to-end; continuously iterate based on real usage and failure modes.

View detail

As a Backend Engineer, AI, you own the inference and orchestration layer that powers every AI interaction in the product. You build and operate backend systems that serve AI-powered features in production, design inference pipelines, orchestration layers, and service boundaries around models. You are responsible for production concerns such as monitoring, logging, alerting, and incident response. Additionally, you optimize latency and throughput across inference, caching, batching, and streaming. Your work enables backend systems to run reliably at scale, handling production AI traffic with low latency and high throughput, ensuring APIs are stable, clear, and support seamless integration with frontend and ML systems. You ensure production incidents are quickly detected, diagnosed, and resolved, minimizing user impact, and continuously improve system performance and reliability through iterative changes based on real usage.

View detail

Collaborate with the AI Tools squad to implement and improve AI features in the Photoroom app, including Logo maker, AI Images, AI Videos, and other features on the app homepage. Design and architect new features by chaining a mix of internal and external services to generate images and videos for users. Monitor and scale the growing load on the FastAPI service using Datadog to find optimizations and bottlenecks or implement smart caching of pipeline steps.

View detail

Design and build the machine learning infrastructure that powers OpenAI's monetization and ads systems. Develop large-scale data pipelines processing impressions, clicks, conversions, advertiser data, marketplace signals, and other inputs used to train and improve ML models. Create scalable model training platforms for ranking, conversion prediction, quality prediction, bidding, targeting, measurement, and optimization workloads. Develop systems to safely and reliably move models from experimentation into production environments. Build and improve real-time inference and serving infrastructure with strict requirements for latency, throughput, reliability, and availability. Design experimentation frameworks enabling A/B testing, holdouts, model comparisons, ramping strategies, and measurement at scale. Improve platform performance by optimizing training efficiency, inference latency, model throughput, infrastructure reliability, and cost effectiveness. Collaborate closely with ML engineers, product engineers, data scientists, and monetization teams to accelerate development and deployment of advertising systems.

View detail

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 for both internal teams and external partners. Collaborate closely with product, data, and policy teams to translate nuanced safety needs into scalable, maintainable software systems.

View detail

As a Staff/Principal-level Technical Lead, you will be responsible for driving the end-to-end technical execution of multiple concurrent enterprise engagements in close partnership with the Project Lead, from technical discovery to production deployment. You will architect and implement secure, highly scalable integrations between the AI platform and clients' existing data pipelines, APIs, and infrastructure. You will lead technical discovery sessions, architecture workshops, and data readiness assessments with customer IT, data, and engineering leadership teams. You will build and customize AI-enabled solutions, scripts, and workflows that address complex business problems identified in the sales process. You will serve as the primary technical liaison and escalation point between customer engineering teams and internal product, engineering, and data science teams to unblock deployments quickly. You will ensure that all deployed solutions meet enterprise-grade standards for performance, security, data privacy, and scalability. You will debug complex integration issues, manage technical risks across overlapping projects, and provide hands-on troubleshooting during implementation. Additionally, you will contribute to the internal codebase by documenting technical blueprints, developing reusable integration components, and providing product feedback based on real-world edge cases.

View detail

Design, implement, and operate cutting-edge ML Training OR Inference performance optimization techniques to scale VLM, VLA, and Foundational models and deploy them efficiently in robotaxis. Collaborate closely with cross-functional teams, including ML researchers, software engineers, data engineers, and hardware engineers, to define requirements and align on architectural decisions.

View detail

As a Senior Software Engineer (AI Data & Evaluation) at Mercor, you will build the data infrastructure and evaluation systems for frontier AI models, develop evaluation methodologies and flywheels to improve data quality and model performance, design and build synthetic data generation systems and simulation environments producing high-signal, high-diversity training data, architect and ship operational automation systems to maximize throughput, efficiency, and quality, collaborate cross-functionally with Operations, Research, and Product teams to translate evolving model needs into scalable engineering solutions, and own the end-to-end delivery of critical systems from prototyping to scaling production infrastructure.

View detail

Define and implement comprehensive quality assurance strategies and test plans for AI agents and LLM-powered applications to ensure product reliability and performance. Design and develop automation frameworks, creating robust, scalable, and maintainable automated test frameworks or enhancing existing ones using languages such as Typescript and Python. Collaborate with product managers, machine learning engineers, and data scientists to understand AI features and model behaviors, translating these into test cases and validation criteria. Drive continuous improvement of testing processes and infrastructure by integrating automated checks within CI/CD pipelines for rapid, high-quality releases. Identify, document, and track software defects and inconsistencies, performing root cause analysis to provide actionable feedback to development teams. Monitor production systems and AI model performance to identify potential issues and contribute to post-release quality validation. Champion quality best practices across engineering teams, fostering a culture of ownership and continuous improvement. Design, manage, and maintain test data strategies and mock services to ensure stable, isolated, and repeatable test execution. Design, develop, or integrate agentic AI systems, AI skills, and the Model Context Protocol (MCP). Manage the full defect lifecycle by analyzing customer feedback and debugging logs to identify, prioritize, and track software bugs, collaborating with development teams to ensure timely resolution.

View detail

Define and implement comprehensive quality assurance strategies and test plans for AI agents and LLM-powered applications to ensure product reliability and performance. Design and develop automation frameworks by creating robust, scalable, and maintainable automated test frameworks or enhancing existing ones, requiring proficiency in languages such as Typescript or Python. Collaborate closely with product managers, machine learning engineers, and data scientists to understand complex AI features and model behaviors, translating these into effective test cases and validation criteria. Drive continuous improvements in testing processes and infrastructure by integrating automated checks within CI/CD pipelines for rapid, high-quality releases. Identify, document, and track software defects, performing root cause analysis to provide actionable feedback to development teams. Monitor production systems and AI model performance to proactively identify potential issues and contribute to post-release quality validation. Champion quality best practices across engineering teams to foster ownership and continuous improvement in delivering AI solutions. Design, manage, and maintain test data strategies and mock services to ensure stable, isolated, and repeatable test execution. Manage the full defect lifecycle by analysing customer feedback and debugging logs to identify, prioritise, and track software bugs, collaborating closely with development teams for timely resolution. Additionally, have experience designing, developing, or integrating agentic AI systems, AI skills, and the Model Context Protocol (MCP).