AI Jobs in San Jose

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As the Defense / Edge Tech Lead, you will own the technical direction for deploying Deepgram's speech-to-text (STT) and text-to-speech (TTS) models to edge and embedded environments. Your responsibilities include leading the technical strategy for edge deployment, defining the architecture for on-device, on-premises, and air-gapped inference across diverse hardware targets. You will optimize models for edge and embedded platforms through quantization, pruning, distillation, and runtime optimization to meet latency, memory, and power constraints. You will partner with hardware vendors like Qualcomm and Motorola for SDK integration, performance benchmarking, and joint go-to-market efforts. Supporting defense customer requirements through AWS NatSec partnerships by translating mission requirements into engineering deliverables is also part of your role. You will design and build edge runtime infrastructure such as model packaging, deployment pipelines, OTA update mechanisms, and telemetry for devices in low or no connectivity environments. Deployments must be hardened for security-sensitive environments with features like secure boot chains, encrypted model storage, tamper detection, and audit logging. You will benchmark and validate performance across hardware platforms, establishing test suites for latency, accuracy, power consumption, and resource utilization. Collaboration with Research and Engine teams to influence model architectures toward edge-friendly designs is expected. Furthermore, you provide technical leadership to cross-functional teams on defense and edge projects, set engineering standards, review designs, and mentor engineers on systems and optimization practices.

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Contributors design original computational chemistry problems that simulate real chemistry research workflows and create problems requiring Python programming to solve using libraries such as numpy, scipy, and chemical libraries. They ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days or weeks), develop problems requiring non-trivial reasoning chains in physical chemistry, quantum chemistry, and molecular modeling, base problems on real research challenges or practical applications from chemistry practice, verify solutions using Python with standard computational chemistry approaches, and document problem statements clearly while providing verified correct answers.

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Contributors may design original computational mathematics problems that simulate real mathematical research workflows, create problems requiring Python programming to solve (using Numpy, SciPy, Sympy), ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks), develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis, base problems on real research challenges or practical applications from mathematical practice, verify solutions using Python with standard mathematical libraries, and document problem statements clearly while providing verified correct answers.

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Design original computational mathematics problems that simulate real mathematical research workflows; create problems requiring Python programming to solve using libraries such as Numpy, SciPy, and Sympy; ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes; develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis; base problems on real research challenges or practical applications from mathematical practice; verify solutions using Python with standard mathematical libraries; document problem statements clearly and provide verified correct answers.

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Design original computational mathematics problems simulating real mathematical research workflows; create problems requiring Python programming to solve using libraries such as Numpy, SciPy, and Sympy; ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes; develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis; base problems on real research challenges or practical applications; verify solutions using Python with standard mathematical libraries; document problem statements clearly and provide verified correct answers.

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Translate business requirements into requirements for AI/ML models, prepare data to train and evaluate AI/ML/DL models, build AI/ML/DL models by applying state-of-the-art algorithms including transformers and leveraging existing algorithms from academic or industrial research, test and evaluate AI/ML/DL models, benchmark their quality, and publish the models, data sets, and evaluations, deploy models in production by containerizing them, work with customers and internal employees to refine model quality, establish continuous learning pipelines for models using online learning or transfer learning, and build and deploy containerized applications on cloud or on-premise environments.

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The Senior Data Intelligence Engineer is responsible for building and maintaining high-fidelity dbt and SQL models that serve as the foundational data for complex, usage-based revenue models. They develop tools and permissions frameworks enabling 'Analyst Agents' to query data sources such as Athena, correlate Salesforce churn signals, and identify API latency issues. The engineer acts as the technical liaison with the Engineering/Infrastructure team to ensure data contracts are reliable and ready for autonomous agents. They partner with the Head of Data to ingest and transform thousands of hours of unstructured internal call audio into queryable insights for go-to-market teams using Deepgram’s own models. The role includes maintaining a culture focused on automating manual and repetitive SQL tasks through code and agent systems rather than legacy dashboards.

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As an Applied AI Engineer, responsibilities include building and shipping AI features end-to-end from model to system to user experience, designing and iterating on prompts, tools, memory, and agent workflows, turning raw model outputs into structured, reliable, and predictable behaviors, debugging issues across the full stack including model, orchestration, infrastructure, and UX, optimizing for latency, cost, and production reliability, developing lightweight evaluation frameworks to measure real-world performance, and working closely with product and engineering teams to translate ambiguous problems into working systems.

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Build and deploy AI agents including prompt design, workflow configuration, integrations, telephony setup, and evaluation frameworks. Act as the primary technical partner for customers by leading regular demos, communicating progress, gathering feedback, and guiding solutions from concept to production. Configure and connect systems using APIs to handle authentication, data mapping, error handling, and integrate with CRMs, knowledge bases, and other enterprise tools. Set up telephony systems including SIP/CCaaS/PSTN routing, metadata passing, fallback configurations, and troubleshooting call quality. Write and refine prompts for LLM-driven agents, monitor performance, test iteratively, and ensure agents meet automation and containment targets. Translate customer requirements into actionable solutions and work consultatively to unblock challenges related to security, connectivity, or knowledge ingestion. Collaborate cross-functionally with product and engineering teams to escalate platform gaps, resolve technical issues, and independently drive leading client implementations.

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Design, automate, maintain, and optimize Python ETL pipelines (Spark/Ray) for large-scale multimodal data. Build and maintain data cataloging, lineage, quality tooling, integrity verification, access controls, and lifecycle management systems. Provide guidance, internal tools, and documentation to colleagues on data best practices. Serve as a custodian of the company’s datasets, ensuring overall data health, quality, and discoverability.