Statistics Expert (Python) - Freelance AI Trainer
Contributors may design rigorous statistics problems reflecting professional practice, evaluate AI solutions for correctness, assumptions, and constraints, validate calculations or simulations using Python libraries such as NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn, improve AI reasoning to align with industry-standard logic, and apply structured scoring criteria to multi-step problems.
Statistics Expert (Python) - Freelance AI Trainer
Contributors may design rigorous statistics problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python (NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn); improve AI reasoning to align with industry-standard logic; and apply structured scoring criteria to multi-step problems.
Engineering Manager, Active Learning
The Engineering Manager role at Deepgram involves leading the design and implementation of internal data and ML training systems. Responsibilities include recruiting, hiring, training, and supporting top engineering talent to build a world-class team; transforming cross-functional visions into detailed project plans with clarity on commitments, risks, and timelines; defining and owning technical strategy to accelerate ML training pipelines; promoting a strong team engineering culture focused on rigorous engineering standards and continuous improvement; partnering with DataOps and Research teams to design and implement new services, features, or products end to end; and coaching and mentoring engineers to support personal growth while achieving ambitious team goals.
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.
Research Engineer, Machine Learning Systems
The responsibilities include architecting and managing horizontally scalable systems to accelerate the end-to-end training lifecycle for Speech-to-Text (STT) and Text-to-Speech (TTS) models, focusing on optimized data preparation, high-throughput training pipelines, distributed infrastructure, and automated evaluation tooling. The role also involves designing and implementing internal UIs and tools to make ML systems and workflows accessible and transparent to non-technical stakeholders. Additionally, the position requires overseeing and managing training tooling, job orchestration, experiment tracking, and data storage.
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.
Physics Researcher (Python) - Freelance AI Trainer
Design rigorous physics problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python (NumPy, Pandas, SciPy); improve AI reasoning to align with industry-standard logic; apply structured scoring criteria to multi-step problems.
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.
Mathematics Researcher (Python) - Freelance AI Trainer
Contributors may design rigorous mathematics problems reflecting professional practice, evaluate AI solutions for correctness, assumptions, and constraints, validate calculations or simulations using Python (NumPy, Pandas, SciPy), improve AI reasoning to align with industry-standard logic, and apply structured scoring criteria to multi-step problems.
Solutions Architect (NYC)
The Solutions Architect is responsible for designing scalable, highly-available infrastructure for AI platform deployments including compute, storage, networking, security, enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines. They design multi-agent systems using different patterns, implement agent logic using modern frameworks such as langchain/langgraph, design comprehensive evaluation frameworks, optimize prompts with A/B testing, and guide deployment and operations. They lead technical maturity assessments, work directly with enterprise customers to understand requirements and present recommendations, and partner with Engagement Managers and Product/Engineering teams.
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