Go AI Jobs

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As the first US based Engineer, the role entails acting as the technical bridge between the product vision and customer reality. Responsibilities include designing, architecting, and shipping full-stack features that solve customer compliance challenges, owning the technical relationship with key customers by implementing solutions, gathering requirements, and translating feedback into product improvements. The engineer will build scalable services and APIs for the LLM compliance platform, make high-impact technical decisions quickly while being accountable to engineering standards and customers, challenge assumptions about what and how to build, and shape the product roadmap and engineering practices as the company scales from Series A to market leadership. The work combines coding, customer engagement, and steering product direction with high autonomy and collaboration with the founding team.

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Debug and fix issues in the platform and ship pull requests with those fixes. Build internal tools and copilots powered by generative AI to enhance the team’s capabilities. Rapidly prototype proof-of-concepts for customer use cases. Work collaboratively across Engineering, Product, and Solutions teams to unblock customers and advance AI adoption.

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Design, architect, and operate scalable services and APIs that power the LLM compliance platform. Architect how AI insights are surfaced to users, ensuring the system is robust, fast, and intuitive. Make high-impact technical decisions quickly. Challenge "why" and "how" to ensure delivery of the best possible experience for users. Shape engineering culture, standards, and tooling as the company grows. Own end-to-end technical decisions including designing systems, architecting solutions, shipping to production, and iterating based on customer feedback.

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Design, build, and maintain scalable machine learning systems including data ingestion, preprocessing, training, testing, and deployment. Develop and optimize end-to-end ML pipelines encompassing data collection, labeling, training, validation, and monitoring to ensure reliability and reproducibility. Implement robust MLOps practices such as model versioning, experiment tracking, CI/CD for machine learning, and continuous monitoring in production environments. Collaborate with product and engineering teams to integrate and deploy models into real-time products with a focus on efficiency and scalability. Ensure data quality, observability, and performance across all AI systems. Stay current with the latest AI infrastructure, tooling, and research to support ongoing innovation.

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As a member of the Bots team, design, build, and scale systems that enhance user engagement with the AI-powered platform, including bot chat orchestration, AI image generation, AI video generation, and tooling for managing these features. Collaborate with cross-functional teams like product managers, designers, and data specialists to deliver high-quality, performant, and maintainable features. Experiment with and integrate new AI image, video, and voice generation technologies. Build tooling and infrastructure around various AI technologies. Gain exposure to the architecture and operations of a fast-growing social AI product. Contribute expertise to evolve team processes and technical infrastructure, ensuring scalability and reliability.

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As a Staff Software Engineer on the Core Infrastructure team at Harvey, your responsibilities include designing and building scalable, fault-tolerant infrastructure systems that power Harvey's AI platform across multiple cloud regions. You will own and evolve the multi-cloud infrastructure (Azure, GCP), including Kubernetes orchestration, networking, and container management. You will lead technical initiatives focused on observability, incident response, and operational excellence, building systems for rapid detection and resolution of issues. Architecting and optimizing distributed systems for reliability, including load balancing, quota management, and failover mechanisms, will be part of your role. You will partner with Product Engineering and Security teams to ensure infrastructure accelerates product development, drive infrastructure-as-code practices using tools like Terraform and Pulumi for reproducible deployments, and mentor engineers through code reviews, design reviews, and technical leadership. Representative projects include designing model proxy architecture for handling inference requests, building distributed rate limiting and quota management systems, architecting multi-region deployment strategies for data residency compliance, developing observability infrastructure with SLA monitoring and cost tracking, and leading CI/CD pipeline evolution to improve velocity and stability.

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Debug and fix issues in the platform and ship PRs with fixes. Build internal tools and copilots powered by generative AI to enhance the team. Rapidly prototype proof-of-concepts for customer use cases. Collaborate across Engineering, Product, and Solutions teams to unblock customers and drive AI adoption.

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The Staff Software Engineer is responsible for owning the end-to-end technical vision for the EDR telemetry and detection workstream, rallying the team from concept through shipping, iterating, and deprecating. This includes producing production code contributions in a modern backend language such as Go, Rust, or Python within a service-oriented environment and setting technical standards through design reviews, code quality, and operational discipline by example. The role involves mentoring engineers, building frameworks and architecture to enable high performance, partnering with the hiring team on recruiting and leveling engineers, and holding the team accountable for outcomes by managing risks and tradeoffs early and in writing. The engineer translates ambiguous product goals into concrete technical roadmaps, makes decisions regarding build versus buy or integration with business context, partners closely with product management in PRD reviews and sprint planning, and sequences MVP development effectively. Domain expertise is required in EDR platforms including telemetry, API level, detection logic, alert triage, and SOC team workflows. The engineer builds ground truth datasets, manages false positive and false negative tradeoffs and confidence scoring, and owns the detection and measurement methodology, including ground truth methodology, confidence scoring, calibration, and defining what constitutes correct tuning recommendations. The position requires collaboration, and contributing both to leadership and hands-on coding, and may include up to 10% travel.

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As a Software Engineer on Applied AI, you will build, deploy, and operate systems that interface directly between frontier AI research and data delivery. Responsibilities include partnering closely with frontier AI labs to understand their data, post-training, and evaluation needs; building and operating scalable data pipelines for post-training workflows and model evaluations; designing and building scalable systems for synthetic data generation and data quality; working directly with customers to understand requirements and develop technical solutions; prototyping new data types, benchmarks, and evaluation frameworks; and leading technical discussions with customers. You will own projects end-to-end including requirements gathering, creating data creation pipelines, and improving model-adjacent infrastructure while collaborating with frontier AI labs and internal teams to deliver high-impact applied AI solutions.

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Design and deliver production-grade AI agents that are central to Sierra's growth, ensuring they are highly performant, reliable, and scalable in production environments across industries such as finance, healthcare, and commerce. Drive the Agent Development Life Cycle (ADLC) with complete ownership and autonomy from initial pilot through deployment and continuous iteration, building, tuning, and evolving AI agents in production and defining ADLC best practices. Partner directly with leaders at large enterprises and cutting-edge startups to understand their business challenges and build AI agents that transform operations at scale. Build the future of Sierra's platform by gathering customer feedback, prototyping new tools and features, and collaborating with research, product, and platform teams to enhance AI agent development and Sierra's product. Work on projects including designing AI agents to manage subscription churn, developing agents for complex customer interactions, creating industry-specific AI agent frameworks, facilitating design partnerships for new product initiatives, and experimenting with latest voice models for enterprise-grade integration.