model-deployment jobs in San Diego

As an ML engineer at Booz Allen Hamilton, you'll design, build, and deploy production-grade AI/ML systems for Defense and Intelligence clients. You'll work across the full ML lifecycle—from model development in deep learning, computer vision, NLP, and signal processing through MLOps, containerization, and cloud deployment.…See more The role requires 2+ years of ML engineering or data science experience, proficiency with frameworks like TensorFlow and PyTorch, and the ability to obtain a Secret clearance.

Senior engineer responsible for designing, building, and optimizing large-scale AI platforms for wireless, 5G, and connected devices. You will architect end-to-end AI/ML pipelines, integrate models into chipsets and cloud/edge environments, and deliver scalable inference services, tools, and SDKs.…See more Key responsibilities include model deployment, performance tuning on heterogeneous hardware, MLOps automation, and monitoring across silicon, software, and product teams.

As a Machine Learning Engineer at Qualcomm, you will design, develop, and deploy AI and Generative AI solutions for the Nimbus product line, focusing on network optimization, Digital Twin technologies, and LLM-based applications. You will collaborate with cross-functional engineering, product, and domain teams to translate business requirements into production-ready AI-enabled solutions, staying current with emerging AI technologies and best practices.…See more The role requires hands-on experience with model development, evaluation, deployment, and operationalization, along with strong software engineering practices for scalable and maintainable systems. You must communicate technical concepts effectively to both technical and non-technical audiences.

Qualcomm seeks a Machine Learning Engineer to design, develop, and deploy generative AI solutions focused on Large Language Models, Retrieval-Augmented Generation (RAG), and intelligent agent systems. The role involves implementing RAG-based solutions, optimizing LLM fine-tuning strategies, building evaluation frameworks, and maintaining agentic workflows for autonomous AI systems.…See more The candidate must have strong Python programming skills, practical LLM deployment experience, and familiarity with ML frameworks like PyTorch and TensorFlow, alongside knowledge of vector databases, cloud platforms (AWS, GCP, Azure), and modern MLOps infrastructure.

Lead engineering and data science roadmap for Teradata's AI Platform, overseeing architecture, LLM integrations, RAG pipelines, vector store implementations, and data science experimentation frameworks. Partner with product, research, and engineering teams to define requirements for predictive modeling, multi-agent collaboration, and tool orchestration.…See more Manage and mentor a team of backend engineers, data scientists, and AI platform specialists while driving technical excellence, cloud-native practices, and platform scalability.

Lead a specialized team of software engineers at the intersection of hardware, software, and AI, focusing on developing innovative Pixel experiences using sensor signals. The role requires deep expertise in real-time embedded software development, sensor fusion algorithms, machine learning infrastructure optimization, and AI/ML design.…See more You will independently design and implement systems, collaborate with stakeholders on technical direction, and manage a team building power-efficient, sensor-driven features leveraging computer vision, signal processing, and estimation theory.

Design, build, and operate production ML systems across the full lifecycle—from data preparation and model training through deployment, monitoring, and continuous improvement. Partner with data scientists, software engineers, and platform teams to turn research prototypes into reliable, scalable services.…See more Work across recommendation, ranking, forecasting, classification, NLP, and generative AI depending on product priorities. Balance model quality, inference latency, scalability, and operational resilience as equally important outcomes.

Senior AI Software Engineer responsible for designing, developing, and deploying LLM-powered applications and AI-assisted development workflows within a large-scale enterprise Linux environment (~6M+ LOC, primarily C/C++). Build high-performance GPU-based inference pipelines using vLLM and modern frameworks, develop agentic AI workflows with RAG and tool calling, and integrate LLMs with vector databases and enterprise systems into production.…See more Collaborate across software engineers, AI researchers, and platform teams to productionize AI services while optimizing for performance, latency, scalability, and operational efficiency. Requires 4+ years software development, strong Linux/Red Hat experience, advanced C/C++, Python, LLM/RAG expertise, and GitLab CI/CD workflows.

Google seeks a Software Engineer III to develop computer vision and computational photography features for Pixel camera autofocus, exposure, and white balance systems. You will own the end-to-end product lifecycle from research and algorithm development through prototyping, optimization, and commercialization.…See more The role requires proficiency in C++, machine learning infrastructure (model deployment, optimization, data processing), and computer vision or imaging platforms, along with experience in large-scale system design and performance analysis.

Senior Associate AI Engineer will develop GenAI/LLM applications and integrations using foundational models, build and test AI models and conversational agents, and configure AI solutions into enterprise applications on cloud platforms. The role requires assisting with data preparation for model training, documenting technical workflows, and staying current on emerging AI tools.…See more Requires minimum three years of professional or academic experience in AI/ML, data science, or software engineering, proficiency in Python or similar languages, and hands-on experience with a major cloud AI platform (Azure AI, AWS Bedrock, or Google Cloud Vertex AI).

Senior Machine Learning Engineer responsible for architecting, designing, developing, and deploying on-device AI prototype software for edge computing applications. The role requires expert-level proficiency in C++ and Python, deep knowledge of ML frameworks like PyTorch, and hands-on experience with low-level system debugging, performance tuning, and native inference driver development.…See more You will work in a multi-disciplinary research team advancing generative AI technology for the edge, including model fine-tuning, hardware acceleration, model quantization, and edge inference. A strong theoretical background in deep learning combined with embedded software development experience and modern software engineering best practices is essential.

Senior Engineer, Machine Learning at Element Biosciences will design, develop, and optimize deep learning models (CNNs, Vision Transformers, U-Net) for biological image analysis and deploy them to production on AWS or imaging instruments. Responsibilities include building end-to-end ML pipelines from data ingestion through inference, applying advanced image processing and computer vision techniques to multimodal biological images, and analyzing single-cell and multiomic data.…See more The role requires 5–7 years of experience with a Master's degree (or 0–3 years with a PhD) and hands-on proficiency in PyTorch, Python, and cloud deployment, with strong preference for experience in biomedical image modalities.

Lead the design and delivery of enterprise AI platforms and applications on Google Cloud, leveraging Vertex AI, Gemini, and cloud-native technologies. Design, fine-tune, and govern LLM solutions; build RAG and agentic systems; and define end-to-end architectures spanning data pipelines, feature engineering, model lifecycle, APIs, and MLOps/LLMOps.…See more Architect cloud-native applications on GKE, Cloud Run, and managed services while implementing security, governance, and production-grade monitoring for AI/ML systems at scale.

The AI Architect II is a senior technical leadership role responsible for establishing architectural standards and patterns for AI solutions, including RAG pipelines, agentic workflows, and model serving. The role involves designing scalable AWS cloud architectures, defining MLOps practices, implementing responsible AI governance, and translating business requirements into enterprise AI and cloud strategies.…See more The position requires hands-on technical expertise to evaluate and integrate AI/ML services, lead systems design across microservices and event-driven architectures, and mentor engineering teams on cloud and AI best practices. This role drives technology decisions with real business impact while modernizing the company's platform through cloud-native and AI transformation initiatives.

Lead a specialized team of software engineers developing innovative Pixel sensor experiences at the intersection of hardware, software, and AI. You will drive the design and implementation of real-time embedded systems that process sensor signals efficiently while applying machine-learning and signal-processing algorithms in a power-optimized manner.…See more Required: 8 years software development experience, 5 years in computer vision or signal processing, 5 years leading ML design and infrastructure optimization, and 3 years technical leadership. This role demands deep expertise in embedded software, AI/ML algorithms, sensor fusion, and the ability to independently architect systems while managing a team.

The Applied AI Engineer develops, deploys, and maintains generative AI solutions for insurance products and internal workflows. The role involves implementing end-to-end AI pipelines using AWS services (SageMaker, Lambda, ECS/EKS, S3), building feature engineering workflows in Snowflake, and collaborating with cloud and MLOps teams.…See more Candidates need 3+ years of AI/ML engineering experience with 1–2 years focused on generative AI or LLMs, proficiency in Python and frameworks like PyTorch or Hugging Face Transformers, and hands-on cloud deployment experience. The position requires monitoring model performance, ensuring security and compliance, and supporting AI architecture reviews.