Backend Engineer- Inference Services
The Backend Engineer is responsible for leading the design and implementation of Deepgram's products, specifically developing secure, robust, and scalable services for speech processing, distributed compute orchestration, and optimized scheduling. Responsibilities include improving Deepgram's core inference services in networking, speech processing, audio transcoding, and latency and memory optimization, developing processes for measuring, building, and optimizing services to maximize system performance, debugging complex system issues involving networking, scheduling, and high performance computing, rapidly customizing backend services to support customer needs, and partnering with Product to design and implement new services, features, and products end to end.
Engineering Manager, Go - Assist & Chat
Own the observability and lifecycle management of AI features across the organization. Build tools and infrastructure to enable teams to develop, monitor, and optimize LLM-powered features. Design and implement closed-loop evaluation pipelines that automatically validate prompt changes. Develop comprehensive metrics and dashboards to track LLM usage including cost per feature, token patterns, and latency. Create systems that tie user feedback to specific prompts and LLM calls. Establish best practices and processes for the full lifecycle of prompts, including development, testing, deployment, and monitoring. Collaborate with engineering teams across the organization to ensure they have the tools and visibility needed to build high-quality AI features.
Head of Internal Tools Engineering
The Head of Internal Tools Engineering is responsible for owning the end-to-end strategy and roadmap for all internal tools, platforms, and automation, treating internal technology as a product. They make strategic build-vs-buy decisions, map current and next-state process flows, and lead systems transformation for internal teams. They architect and maintain the full engineering lifecycle of internal platforms, build seamless API-first ecosystems integrating various internal systems, ensure system reliability and operational resilience, and design scalable, secure architectures using cloud-native principles and microservices. They lead AI strategy by integrating AI and LLMs into internal workflows and deploying intelligent automation tools. They reduce cognitive load for internal users by providing standardized workflows and self-service capabilities, measure platform success by adoption, satisfaction, and productivity impact, and build, lead, and mentor a high-performing engineering team. They cultivate a collaborative culture, provide technical mentorship, foster psychological safety, partner cross-functionally with leadership across departments, and align internal platform investments with company strategy while demonstrating measurable ROI.
Head of Internal Tools Engineering
The role involves architecting, building, and scaling the internal technology ecosystem to accelerate workforce productivity, eliminate operational friction, and provide a compounding infrastructure advantage by treating internal tools with product rigor and user-centricity. Responsibilities include owning the end-to-end strategy and roadmap for all internal tools, platforms, and automation; making strategic build-vs-buy decisions; mapping current and next-state process flows and leading systems transformation. The role requires architecting and maintaining the full engineering lifecycle of internal platforms, building API-first ecosystems integrating with various business systems, owning system reliability and operational resilience, and designing scalable, secure cloud-native architectures. The role leads AI adoption and automation integration into internal workflows, including deploying intelligent automation tools, evaluating AI-assisted troubleshooting, and driving continuous experimentation with prototypes. The person will reduce cognitive load for internal users by providing golden paths and standardized workflows, ensuring frictionless onboarding, and measuring platform success via adoption rates, user satisfaction, DORA metrics, and productivity impact. Team leadership duties include building, leading, and mentoring engineers and managers, fostering a collaborative culture rooted in ownership, speed, craftsmanship, and psychological safety. The role partners cross-functionally with various company leadership teams to translate business needs into a unified technical vision, aligning internal platform investments with company strategy and demonstrating measurable ROI.
Freelance AI Evaluation Engineer (Python/Full-Stack)
Create challenging coding test cases that push AI coding systems to their limits. Review and refine realistic coding tasks based on provided production codebases with realistic scope, requirements, and information sources. Write comprehensive functional tests that validate actual end-to-end behavior and edge-cases, not just superficial checks. Craft fair but hard challenges where the AI has all the context it needs but must work for it, involving information scattered across files and external sources and requiring complex reasoning. Analyze AI failures to understand what the model struggles with versus what it masters. Iterate based on feedback from expert QA reviewers who score work on seven quality criteria.
Freelance AI Evaluation Engineer (Python/Full-Stack)
Create challenging coding test cases that push AI coding systems to their limits by reviewing and refining realistic coding tasks based on provided production codebases with realistic scope, requirements, and information sources. Write comprehensive functional tests that validate actual end-to-end behavior and edge-cases, not just superficial checks. Craft "fair but hard" challenges where the AI has all the context it needs but must work for it, involving information scattered across files and external sources and requiring complex reasoning. Analyze AI failures to understand areas where the model struggles versus what it masters. Iterate based on feedback from expert QA reviewers who score the work on seven quality criteria.
Freelance AI Evaluation Engineer (Python/Full-Stack)
Create challenging coding test cases that push AI coding systems to their limits by reviewing and refining realistic coding tasks based on provided production codebases with realistic scope, requirements, and information sources. Write comprehensive functional tests that validate actual end-to-end behavior and edge cases, not just superficial checks. Craft "fair but hard" challenges where the AI has all the context it needs but must work for it, involving information scattered across files and external sources and complex reasoning. Analyze AI failures to understand what the model struggles with versus what it masters. Iterate based on feedback from expert QA reviewers who score work on seven quality criteria.
Freelance AI Evaluation Engineer (Python/Full-Stack)
Create challenging coding test cases to push AI coding systems to their limits by reviewing and refining realistic coding tasks based on provided production codebases with realistic scope, requirements, and information sources. Write comprehensive functional tests that validate actual end-to-end behavior and edge-cases. Craft challenges that are fair but hard, where the AI has all the context it needs, requiring complex reasoning with information scattered across files and external sources. Analyze AI failures to understand the model's struggles and strengths. Iterate based on feedback from expert QA reviewers who score work on seven quality criteria.
Freelance AI Evaluation Engineer (Python/Full-Stack)
Create challenging coding test cases that push AI coding systems to their limits by reviewing and refining realistic coding tasks based on provided production codebases. Write comprehensive functional tests that validate actual end-to-end behavior and edge cases, craft fair but hard challenges requiring complex reasoning and scattered information, analyze AI failures to understand model strengths and weaknesses, and iterate based on feedback from expert QA reviewers who score work on seven quality criteria.
Freelance AI Evaluation Engineer (Python/Full-Stack)
You will create challenging coding test cases to push AI coding systems to their limits by reviewing and refining realistic coding tasks based on provided production codebases with realistic scope, requirements, and information sources. You will write comprehensive functional tests that validate actual end-to-end behavior and edge cases, not just superficial checks. You are to craft "fair but hard" challenges where the AI has all the necessary context but must work through scattered information and complex reasoning. Additionally, you will analyze AI failures to understand what the model struggles with versus what it masters, and iterate your work based on feedback from expert QA reviewers who score your work on seven quality criteria.
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