machine-learning jobs in San Diego

The Bioinformatics Analyst prepares, maintains, and analyzes genomic, transcriptomic, metagenomic, and metatranscriptomic data from sequencing projects. Responsibilities include metagenomic assembly and annotation, statistical and comparative analysis, data mining, database curation, and applying machine learning and explainable AI methods for feature selection and classification.…See more The role works independently on complex tasks while collaborating with cross-functional scientific and technical teams, with potential for project leadership and mentoring as experience grows.

Design, implement, and verify machine learning software for embedded systems running on ARM processors, DSPs, and GPUs in mobile, edge, automotive, and IoT products. Develop optimized ML kernels, compilers, and tools; optimize ML frameworks like TensorFlow and PyTorch for efficient hardware execution.…See more Perform integration, testing, and performance optimization of embedded software subsystems. Collaborate with hardware teams on joint design and development, coordinate cross-team dependencies, and contribute to design reviews.

Shield AI seeks a Staff Engineer, DevOps to own and maintain the build system for autonomous aircraft (XBAT/VBAT), with deep expertise in C++ and CMake. You will improve developer velocity by reducing build/test cycle times, integrating CI pipelines with HIL and simulation environments, and implementing monitoring and dashboards for build system performance.…See more The role requires 7+ years of industry experience (or 5+ with advanced degree), proficiency with containers/orchestration, scripting, CI/CD tools, and cloud operations across Azure/AWS/GCP. You'll collaborate with software engineers to resolve failures, set best practices, and leverage agentic AI solutions to identify root causes and accelerate development cycles.

Qualcomm seeks an experienced Staff Product Manager to define and drive the Linux platform roadmap across Snapdragon-based AI PCs, Edge AI systems, and enterprise computing solutions. The role spans product strategy, Linux distribution enablement, upstream kernel and driver strategy, and AI developer platform support.…See more You will gather customer and ecosystem requirements, create product roadmaps, collaborate with OEMs, ISVs, Linux distribution partners, and open-source communities to deliver optimized Linux experiences on Snapdragon platforms. Success requires translating market needs into platform requirements, managing cross-functional alignment across hardware, firmware, kernel, and AI software layers.

As Manager, Data Operations, AI and Automation at Measurabl, you will lead end-to-end utility data quality operations for a commercial real estate ESG platform, owning quality standards, customer data health monitoring, and incident resolution. You will design and deploy AI-driven automation (agents, scripts, workflows) to resolve routine data quality issues at scale, eliminating manual work rather than staffing it.…See more You will manage offshore and vendor teams while serving as the technical escalation point for high-impact data quality issues, ensuring customer data meets audit and regulatory standards. Success is measured on team outcomes and automation effectiveness, not personal task routing.

SAIC seeks an experienced System Administrator to maintain and integrate AI capabilities into expeditionary systems for the Navy, including sensors, unmanned systems, command-and-control, and TAK. The role combines hands-on administration (Linux systems, edge servers, software updates, network troubleshooting) with collaboration on machine learning integration, data pipeline monitoring, and tactical system testing at field exercises.…See more The candidate will work across cross-functional teams—including ML engineers, data engineers, and software engineers—to deploy and validate high-performance data flows in operational environments. Requirements include 2+ years in a related technical role (sysadmin, data engineer, software engineer, or similar), intermediate Linux command-line proficiency, familiarity with containerization (Docker, Kubernetes) and edge GPUs (Jetson Nano, DGX), and experience with tactical system troubleshooting and data pipeline deployment.

AMD seeks a Principal Compiler Architect to lead the design and optimization of LLVM-based compilers for AMD GPUs, focusing on performance improvements for ML frameworks and HPC applications. The role involves working with architecture specialists on hardware bring-ups, debugging compiler issues, and developing groundbreaking compilation technologies for current and future GPU hardware.…See more The candidate will apply data-driven optimization strategies, stay current with hardware and software trends, and collaborate across teams to enhance compiler support for ML and HPC workloads. Required expertise includes strong C/C++ programming, understanding of GPU execution models, and ideally a track record of open-source LLVM contributions.

Own end-to-end demand forecasting and capacity planning for AI systems, compute, storage, GPU, and emulation across a global engineering organization spanning tens of thousands of servers and hundreds of petabytes of storage. Translate silicon product roadmaps and tapeout schedules into multi-year demand plans, size and justify capital purchases into the hundreds of millions of dollars, and present capacity positions to engineering and finance leadership.…See more Build and maintain forecasting models, decision rules, and utilization analytics; apply AI and automation to move beyond manual spreadsheet work. Lead and mentor a team of analysts, raising their craft and setting analytical direction.

Sr. Director role overseeing SIE's data and AI platform ecosystem, including data infrastructure (streaming, ingestion, warehouse), experimentation platforms, and governance tools.…See more Responsible for defining long-term vision, leading global engineering teams, and ensuring platforms process massive data volumes to deliver insights across gaming, content creation, and enterprise operations. Must drive scalability, cost efficiency, security, and operational excellence while collaborating with senior leadership to align platform strategy with business objectives. Requires proven Sr. Director-level experience and thought leadership in platform architecture, real-time data processing, and ML/AI integration.

Design and implement Infrastructure as Code to automate provisioning, monitoring, and lifecycle management of NoSQL, Streaming, and Caching platforms (Cassandra, Aerospike, Kafka, Redis) across AWS and GCP. Build highly available, self-healing systems with automated failover and scaling, develop comprehensive observability solutions, and lead incident response for critical data platform issues.…See more Drive automation-first practices and apply AI/ML approaches such as anomaly detection and predictive scaling to enhance reliability and reduce manual toil. Partner with engineering and platform teams to ensure resilient infrastructure supporting billions of transactions and millions of players globally.

Design and architect next-generation ML inference infrastructure for globally distributed, multi-tenant model serving with high availability, scalability, and cost efficiency. Lead the development of low-latency, high-throughput inference systems supporting computer vision and multimodal models (CNNs, segmentation, object detection) using Scala, Java, and Go.…See more Build large-scale distributed systems with reactive frameworks, integrate enterprise feature stores, and extend CI/CD pipelines (GitHub Prow, Pulumi) with automation and policy enforcement. Design advanced observability frameworks, optimize ML algorithms for performance, mentor engineers, and ensure MLOps and compliance standards (GDPR, SOC2) across the platform.

Qualcomm is seeking a software engineer to develop power and limits management software for SoC chipsets, owning the full lifecycle from pre-silicon through commercialization. The role involves analyzing requirements, designing robust software solutions, optimizing for power efficiency, and developing profiling/validation tools across multiple subsystems.…See more Candidates will perform detailed analysis to recommend improvements in performance, memory, and power utilization, including algorithm design and SW/HW partitioning. The position requires hands-on embedded systems expertise, real-time OS knowledge, and the ability to interface across system, power, software, and hardware engineering disciplines.

Design, develop, and optimize machine learning systems for production AI platforms, including model development, inference optimization, and scalable ML infrastructure. Build training-to-deployment pipelines, optimize model serving for latency and cost, and integrate LLMs and generative AI models into microservices and APIs.…See more Develop data pipelines for ingestion, preprocessing, and feature engineering while collaborating cross-functionally with product, platform, and hardware teams to deliver end-to-end ML solutions.

This role involves independently designing, developing, and debugging real-time embedded software, RTOS environments, and microcontroller firmware for wireless chipsets supporting 3G, 4G, 5G, and AI technologies. The engineer will work on driver development, integration, testing, and validation of communication protocols and platforms, while also contributing to data collection frameworks, machine learning modeling, and embedded software algorithm improvements.…See more Responsibilities include on-target development, tool and process optimization, customer support, technical documentation, and active participation in design reviews. The position requires a Master's degree in Electrical Engineering, Computer Engineering, Computer Science, or related field.

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.

As a Senior Systems Engineer for Data Center AI at Qualcomm, you will research, develop, optimize, and validate AI/ML solutions that integrate Qualcomm's hardware accelerators with software and ecosystem to deliver high-performance inference in datacenter environments. You will design and deploy Gen AI and LLM applications, implement fine-tuning and distillation techniques, perform model benchmarking, and drive system-level architecture decisions.…See more You will collaborate across functional teams to meet system-level requirements while staying current with advances in AI/ML models and hardware. Required skills include strong Python proficiency, expertise in ML frameworks (PyTorch, TensorFlow), deep understanding of ML deployment and system performance profiling, and experience with parallel computing.

Own and deliver small to medium machine learning system components from design through production deployment, including building data pipelines, training and evaluating models, and implementing MLOps monitoring. Write high-quality Python code to translate technical requirements into maintainable solutions, working with frameworks like scikit-learn, TensorFlow, PyTorch, and HuggingFace.…See more Design and deploy ML models as microservices, APIs, batch jobs, or streaming components on AWS, with responsibility for model performance metrics, data drift detection, and retraining triggers. Collaborate across Data Engineers, Software Engineers, Data Scientists, and product stakeholders to deliver project objectives.

This role focuses on designing, developing, and deploying next-generation agentic automation solutions that replace and extend traditional RPA capabilities across enterprise systems. The engineer will architect AI-driven agents, chatbots, and workflow engines; lead technical delivery for a global matrixed team; and integrate platforms like Automation Anywhere with modern agentic frameworks and containerized infrastructure.…See more Success requires 7+ years in automation engineering or software development, strong proficiency in Python, Rust, and TypeScript, hands-on experience with agentic runtimes (n8n, LangGraph, Agno), and deep expertise in Docker, Kubernetes, enterprise security, and process optimization.

The AI Architect will lead the design, development, and deployment of enterprise-scale generative AI solutions across Azure and AWS, partnering with business and engineering leaders to define AI strategy and architecture standards. The role requires deep expertise in modern LLMs (Claude, Gemini, OpenAI), multi-cloud AI platforms, and enterprise architecture, with 10+ years in software/cloud architecture and 5+ years architecting AI/ML solutions.…See more Key responsibilities include designing end-to-end GenAI solutions leveraging RAG, agentic AI, and multi-agent systems; establishing cloud-native deployment patterns; and defining governance, compliance, and responsible AI frameworks. The ideal candidate will serve as a trusted technical advisor to executives, mentor engineering teams, and drive AI adoption through reusable frameworks and reference architectures.

As a Staff Software Engineer in Data Platform Engineering, you will design, build, and operate cloud-native platform components and services, taking features from design through production. You'll lead well-scoped technical projects, collaborate with senior engineers on architecture alignment, and ensure reliability and scalability through testing and instrumentation.…See more You'll spend most of your time hands-on building operators, controllers, automation, and platform services that other teams depend on, while mentoring junior engineers. The role requires 8+ years of software development experience (or equivalent with advanced degrees), 5+ years of hands-on Kubernetes experience, and proficiency with at least one major hyperscaler (AWS, Azure, GCP).

Own the complete release and deployment strategy for Elemynt's secure AI infrastructure platform, building systems that ensure every release is versioned, reproducible, observable, and safe to operate across enterprise and scientific computing environments. Design and implement deployment automation, CI/CD pipelines, artifact management, and operational visibility (logs, metrics, alerts) that work reliably in customer-managed and restricted environments.…See more Debug incidents, identify root causes, and systematize fixes into reusable automation. Partner with product and engineering teams to embed deployment readiness into the platform from the start.

Build and operate the data and ML infrastructure powering an AI platform for materials science, owning both sides: data pipelines that ingest and curate large-scale scientific output into training-ready formats, and model packaging, serving, monitoring, and CI/CD systems that move models safely from research to production across customer environments. You will design data ingestion and transformation workflows, implement validation and quality gates, package and version models with reproducible builds, run models through batch and online inference with safe rollout and rollback, monitor for drift and degradation, and build observability and internal tooling for engineering and science teams.…See more The role requires 6+ years shipping production software with deep expertise in data systems, ML infrastructure, containers, orchestration, and observability.

Design, build, and ship LLM-powered capabilities end to end—from prototyping and fine-tuning models to deploying production agents and retrieval systems. Own the full stack: prompt and context engineering, multi-step agent design with tool calling, RAG systems (embeddings, chunking, hybrid search, reranking), fine-tuning on multi-GPU with LoRA/QLoRA, evaluation and tracing infrastructure, and clean APIs for other engineers.…See more Work in a secure, distributed environment where the platform runs on customer compute, cloud, or hybrid setups, requiring expertise with both commercial and self-hosted models.

As Principal Software Engineer for AI & Data Platform, you will architect the data foundation for scientific and engineering R&D platforms, designing scalable data processing patterns, ML training pipelines, and intelligent workflow interfaces. You will own end-to-end responsibilities including data modeling for multi-use analytics and ML, building production training and fine-tuning pipelines, model evaluation and benchmarking, and setting engineering standards for the team.…See more The role requires 10+ years shipping production software, expert-level Python, deep experience with large-scale data systems (object storage, analytical processing, training formats), hands-on ML pipeline development, and the ability to set technical direction in early-stage environments while implementing it yourself.

Build the foundational agentic AI layer for a materials-science platform, including multi-model provider abstraction, agent orchestration with stateful checkpoints, retrieval systems, prompt versioning, and comprehensive tracing and evaluation frameworks. You'll design agents that plan and reason over tool calls in production, implement human-in-the-loop safety gates, and ensure all LLM behavior remains auditable and cost-tracked across customers' secure environments.…See more The role demands deep production experience with agentic and LLM systems: async Python, structured outputs, memory and context management, multi-step workflow orchestration, and evaluation harnesses that catch regressions before deployment.