tensorflow jobs in San Diego

Join XiFin's Advanced Data Analytics team as an Associate Machine Learning Engineer to design, develop, and deploy machine learning models with a focus on LLMs, Generative AI, and Agentic AI in healthcare and revenue cycle domains. You will contribute to MLOps pipelines, perform data preparation and prompt engineering, collaborate with cross-functional teams, and validate models for production use.…See more The role requires a Bachelor's degree in Computer Science, Data Science, or related field, with proficiency in Python, ML libraries (scikit-learn, TensorFlow, PyTorch), and foundational knowledge of GenAI architectures, vector databases, and LLM orchestration frameworks.

Design and implement computer vision features and on-device prototyping using the Snapdragon Vision API for mobile, autonomous vehicles, IoT, and AR/VR platforms. Lead CV feature development from algorithm through commercialization, collaborating with hardware, software, and product teams.…See more Develop technical documentation and sample materials for developer enablement. Requires 3–5 years of hands-on computer vision application development with strong C/C++ and Python proficiency and deep understanding of CV algorithms and deep-learning pipelines.

Design and optimize video analysis, quality assessment, and encoding algorithms for hardware-accelerated video codec solutions (FPGA/ASIC). Develop C-models and firmware implementing deep learning techniques (CNNs, Transformers, MLLMs) for video processing, ROI detection, and frame-level rate control.…See more Collaborate with infrastructure teams to integrate algorithms into production VOD and live-streaming pipelines, validate performance through A/B testing, and participate in objective video quality evaluation using metrics like VMAF, PSNR, and SSIM.

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.

Design, develop, and optimize machine learning systems for production AI platforms, focusing on model development, inference optimization, and scalable ML infrastructure. Responsibilities include building ML pipelines and frameworks, optimizing model inference for latency and cost, integrating LLMs into APIs and microservices, and designing data pipelines for preprocessing and feature engineering.…See more The role requires strong software engineering fundamentals combined with deep ML expertise, working across PyTorch/TensorFlow, model-serving systems, distributed computing, and GPU environments.

Design, develop, and optimize production machine learning systems including model development, inference optimization, and scalable ML infrastructure. Build end-to-end ML pipelines from training through deployment, optimize model inference for latency and cost, integrate LLM/ML models into APIs and microservices, and engineer data pipelines for preprocessing and validation.…See more Requires strong software engineering fundamentals with deep ML expertise, proficiency in Python and systems languages, and experience with ML frameworks, model serving, and distributed computing.

Senior technical leader responsible for integrating AI/ML systems into secure, scalable DoD-compliant environments. Will architect AI system deployments, establish DevSecOps practices, ensure compliance with RMF/NIST 800-53/DISA STIG standards, and provide technical guidance across engineering teams.…See more Requires deep expertise in AI/ML production systems, containerization, CI/CD, and cybersecurity controls, with hands-on experience in Python, Kubernetes, and modern infrastructure tooling.

Lead product strategy and roadmap for AI-centric data center platforms and rack-level systems, including compute nodes, interconnect fabrics, power delivery, and cooling. Define system-level requirements by collaborating with silicon architects, systems engineers, and software teams while engaging directly with hyperscale and enterprise customers to gather feedback and drive early deployments.…See more Track product performance post-launch across system metrics like power efficiency and thermal headroom, and mentor a high-performing product management team.

As a Staff Machine Learning Engineer focused on model optimization, you will create and implement ML techniques, frameworks, and tools enabling efficient discovery and deployment of state-of-the-art ML solutions across mobile, edge, auto, and IoT products. You will model, architect, and develop advanced ML hardware co-designed with software, develop optimized inference/training software and compiler tools, and collaborate with cross-functional teams on joint hardware-software design.…See more The role requires 4+ years of hardware/software/systems engineering experience (or equivalent with advanced degree), deep expertise in ML frameworks (TensorFlow, PyTorch, Keras, Caffe), embedded systems optimization, and programming languages like Python or C++. You will work independently with minimal supervision, provide technical guidance to team members, and influence key organizational decisions through consultation with senior leadership.

Design and develop advanced machine learning models for automotive radar signal processing, including end-to-end ML pipelines from data preprocessing through model training, validation, and performance optimization. Evaluate novel deep learning architectures (CNNs, RNNs, Transformers) for radar data analysis and optimize models for real-time embedded deployment under latency, memory, and power constraints.…See more Bridge research and production by translating state-of-the-art ML techniques into scalable algorithms for radar-based detection, classification, and tracking, with strong requirements in signal processing, statistical analysis, and Python/PyTorch/TensorFlow proficiency.

Design and develop performance modeling platforms and tools for Qualcomm's AI/ML hardware accelerators targeting cloud data server applications. Analyze and optimize LLM and AI workloads on SoC architectures, working across hardware architecture, DDR memory controllers, NoC design, and compiler frameworks.…See more Collaborate with multi-disciplinary hardware and systems engineers to find optimal architecture tradeoffs and improve performance and power efficiency. Apply deep expertise in AI workloads, ML frameworks, computer architecture, and RTL design/verification to build systems that validate LLMs and complex ML networks.

Design and architect AI/ML platforms optimized for Qualcomm's hardware (GPU/NPU) and software stack, with focus on end-to-end performance analysis, algorithm tuning, and system bottleneck reduction. Analyze ML workloads across Computer Vision, Audio, Generative AI, and Agentic AI domains; correlate device performance against pre-silicon predictions; and develop reference implementations and tools for third-party developers.…See more Requires 15+ years in high-performance computing systems engineering or software with 5+ years in AI system optimization, strong expertise in AI frameworks and performance profiling, and ability to lead cross-functional teams and articulate complex technical concepts.

As a Multimedia Engineering Intern at Qualcomm, you will work on system-level architecture, analysis, and optimization of multimedia technologies including camera, video encoding/decoding, audio, speech, GPU, and computer vision across Snapdragon devices. The role focuses on HW/SW co-design and power/performance optimization for billions of devices globally.…See more You'll contribute to projects in graphics, computer vision, imaging, speech/audio, video processing, or software tools, depending on your background and interests. The internship requires 11–14 weeks availability (May–September 2027) and a strong foundation in C/C++, Python, and domain-specific knowledge (signal processing, deep learning, or GPU/graphics experience preferred).

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.

As an AI Performance Engineer at Qualcomm, you will create and implement machine learning techniques, frameworks, and tools for efficient discovery and deployment of ML solutions across mobile, edge, auto, and IoT products. You will model, architect, and develop advanced ML hardware co-designed with software, optimize software for AI model deployment on hardware (kernels, compilers, model efficiency tools), and develop ML techniques into products.…See more You will conduct experiments to train and evaluate ML models, work independently with minimal supervision, and provide technical guidance to team members. The role requires 4+ years of hardware/software/systems engineering experience, proficiency with ML frameworks (TensorFlow, PyTorch, Keras), embedded systems optimization, and programming languages suited for ML (Python, C++, R).

As an LLM Engineer at Qualcomm, you will create and implement machine learning techniques, frameworks, and tools for efficient discovery and deployment of ML solutions across mobile, edge, auto, and IoT products. You will model, architect, and develop advanced ML hardware co-designed with software, develop optimized software for AI model deployment (kernels, compilers, model efficiency tools), and lead ML technique application into products.…See more You will work independently with minimal supervision while providing technical guidance to team members and collaborating across cross-functional teams on joint hardware-software design.

Staff-level ML software engineer responsible for designing, implementing, and optimizing machine learning software for embedded systems including mobile, edge, automotive, and IoT products. Will develop and optimize ML kernels, frameworks (TensorFlow, PyTorch), and compilers in C++/Python to leverage specific hardware features on ARM, DSP, and GPU processors.…See more Responsibilities include performance optimization, integration, testing, tool development, and close collaboration with hardware teams. Requires a Master's degree in EE/CS or related field plus 3+ years of relevant experience.

Design, create, and implement production-grade AI/ML systems for Defense and Intelligence clients. You'll architect scalable machine learning solutions handling fast-moving data, deploy models across multiple data modalities using frameworks like TensorFlow and PyTorch, and integrate solutions into mission environments.…See more The role requires 2+ years of ML engineering or data science experience, proficiency with ML frameworks and programming, and hands-on work in deep learning, computer vision, NLP, or signal processing.

Staff-level machine learning software engineer responsible for designing, implementing, and optimizing ML software for embedded systems including mobile, edge, auto, and IoT products running on ARM, DSP, and GPU processors. Primary responsibilities include developing and optimizing ML kernels and frameworks (TensorFlow, PyTorch) for efficient hardware execution, implementing performance-critical software in C++/Python, and collaborating with hardware teams on joint design and optimization.…See more Will lead complex projects involving integration, testing, and tool development for embedded ML subsystems, coordinate cross-team dependencies, and contribute to design reviews and technical planning.

As a Machine Learning Engineer at Qualcomm, you will develop and optimize machine learning frameworks, tools, and algorithms for deployment across mobile, edge, automotive, and IoT products. You will collaborate with cross-functional teams to design hardware-software co-optimized solutions, build efficient inference and training systems, and create ML kernels and compiler tools.…See more The role requires proficiency in ML frameworks (TensorFlow, PyTorch, Keras), embedded systems optimization, and programming languages like Python or C++, with experience in problem domains such as NLP or multimedia.

Design, implement, and verify machine learning software for embedded ARM processors, DSPs, and GPUs in mobile, edge, automotive, and IoT products. Optimize ML kernels, frameworks (TensorFlow, PyTorch), and compilers to leverage specific hardware capabilities; develop in C++, C, or Python with focus on performance tuning and integration testing.…See more Collaborate with hardware teams on joint design/development, coordinate dependencies across teams, and contribute to design reviews and project meetings.

Qualcomm seeks a Senior AI Performance Architect to design hardware accelerator architectures optimized for AI training at scale. The role involves analyzing GPU/accelerator architectures, defining computational blocks supporting multiple datatypes and precisions, architecting memory and scale-out systems, and codesigning hardware with software/LLM requirements.…See more Responsibilities include competitive analysis, performance modeling, and pre-silicon prediction for ML training workloads. The ideal candidate spans software architecture, algorithm development, kernel optimization, and hardware accelerator design.

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.

Shield AI seeks software engineer interns for a 10–12 week paid program, working on autonomous systems across applications, embedded firmware, machine learning, and systems development. Interns will analyze requirements, write production-quality code in languages like C++, Python, or Java, and ship real features using Agile practices alongside full-time engineers.…See more The role spans user-facing applications, embedded flight software, perception models, and platform tools that support autonomy pipelines. Candidates should have a strong foundation in data structures, algorithms, version control (Git), and collaborative development.

Design and improve autonomous AI trading systems deployed in financial markets, with core work on model architectures, training frameworks, and performance evaluation. You'll integrate novel data sources, optimize models for measurable accuracy gains and reduced training time, and build monitoring systems to detect anomalies in live trading strategies.…See more Requires strong ML fundamentals with hands-on experience in PyTorch, TensorFlow, or JAX, feature engineering, and model optimization; minimum 2 years of ML development experience preferred.