Software Gigs

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.

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. 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. 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. 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. 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. 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. 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. 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.

Staff Software Engineer leading the design, architecture, and operations of PlayStation's Real-Time Analytics Platform (RTAP), a large-scale distributed data system handling high-throughput, low-latency analytics and stream processing. The role involves architecting and evolving streaming and analytics platforms using Apache Flink, Spark, ClickHouse, and Druid; integrating batch and streaming workloads via lakehouse architectures; and embedding AI capabilities to automate engineering workflows and improve platform reliability. You will own critical platform capabilities end-to-end from design through production operations, mentor engineers, establish technical standards, and influence cross-functional technical strategy across PlayStation's data infrastructure.

Staff Software Engineer leading design and evolution of PlayStation's Real-Time Analytics Platform (RTAP), a large-scale distributed data system handling high-throughput, low-latency analytics and stream processing. Responsibilities include architecting stream processing pipelines, optimizing data services using Apache Flink, Spark, ClickHouse, and Druid, integrating batch and streaming workloads with lakehouse architectures, and building AI-powered capabilities for platform automation. The role requires deep expertise in distributed systems, real-time analytics, and mentoring engineering teams while establishing technical standards and best practices across the organization.

The Data Architect is a hands-on technical leader responsible for designing and modernizing enterprise data architecture across on-premises and cloud platforms. The role defines scalable data models, integration patterns, and architectural standards for the Data Lake, Data Warehouse, ODS, and cloud platforms while ensuring security, resilience, and high performance. Working closely with engineering, analytics, and business teams, the architect provides technical guidance on modern data systems, governance frameworks, and ETL/ELT pipelines. The position combines strategic vision with practical experience optimizing structured, semi-structured, and unstructured workloads across SQL, NoSQL, and cloud-native systems.

Ignite Digital seeks an AI Engineer to own the end-to-end machine learning lifecycle—from data preparation and model training through production deployment and monitoring. You will integrate AI models into frontend and backend systems, optimize performance for latency and cost, and collaborate with technical leads and customers to deliver Data and AI solutions. The role requires strong Python and cloud platform expertise (AWS, Azure, or GCP), software engineering fundamentals, and the ability to communicate technical opportunities and limitations across varying levels of technical experience.

Staff-level engineer designing and operating large-scale distributed data platforms powering real-time analytics across PlayStation. You will lead the architecture and evolution of the Real-Time Analytics Platform (RTAP), own critical capabilities end-to-end from design through production operations, and mentor engineers. Key responsibilities include building and optimizing distributed services using Apache Flink, Spark Structured Streaming, ClickHouse, and Apache Druid; integrating batch and streaming workloads with lakehouse architectures; and establishing technical standards for distributed systems, stream processing, and data modeling.

As a Data Developer, you will design, develop, and maintain scalable ETL/ELT pipelines and cloud-based data platforms that integrate enterprise systems and support AI/ML initiatives for the U.S. Navy. You will perform data engineering tasks including cleansing, transformation, and optimization of SQL and Apache Spark workloads on AWS, while also developing predictive models and generative AI solutions using Python-based technologies. You will build interactive dashboards and visualizations in Tableau or Qlik, translating technical findings into actionable insights for stakeholders. An active Secret clearance, U.S. citizenship, and a bachelor's degree in a technical field (or 4+ years of relevant professional experience) are required.

Staff GPU Software Engineer at Qualcomm's Adreno GPU Developer Tools team, designing and developing profiling, performance analysis, and debugging tools for next-generation graphics, games, ray tracing, machine learning, and image/video processing. Responsibilities include GPU hardware/software collaboration on performance validation, feature development, issue debugging, driver instrumentation, and developer ecosystem engagement. Requires 4+ years software engineering experience with strong C/C++ proficiency and GPU hardware/graphics knowledge; experience with graphics APIs (Vulkan, DirectX, Metal, OpenGL), game engines (Unity, Unreal Engine), or GPU computing (CUDA, OpenCL) is a plus.

SAIC seeks a Senior Software Engineer to integrate AI capabilities into expeditionary systems for the Navy, including sensor kits, unmanned systems, command & control, and TAK. The role spans hands-on development—maintaining Zenoh data mesh in Rust, building React frontends in JavaScript, integrating machine learning models, converting tactical message formats—and technical leadership with senior management. Required qualifications include 9+ years as a software engineer, data engineer, or data scientist; expert Python; intermediate Rust or Go; experience with containerization, event streaming platforms (Kafka, Kinesis, RabbitMQ, Zenoh), tactical data standardization, and data pipeline deployment. Successful candidates will collaborate with ML engineers, systems engineers, and field teams to ensure high-quality data flow and operational performance.