Jobs in United States

Software Development Engineer In Test in United States

2,095 active opportunities · Updated October 2026

Explore current software development engineer in test jobs across United States. Filter by work mode, employment type, experience, department, date posted and distance.

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📍 Bellevue, Washington, United States· Full-time· Remote
✓ High-confidence listingCompany trend -93.3%
Quick readStrong listing-quality and freshness signals

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Senior Software Engineer — Cortex Training The Snowflake ML Platform team's mission is to let customers run their most demanding ML/AI workloads inside Snowflake. Cortex Training is our LLM post-training platform: it turns scarce, expensive GPU capacity into a simple, composable service, so customers can adapt open-weight foundation models to their own business problems while we handle the hard distributed-systems parts, including scheduling, orchestration, multi-node training and inference, fault tolerance, and throughput. The platform already runs post-training at scale. Under the hood, it decouples GPU computation from the training loop and exposes it as primitive APIs that compose into everything from SFT to full RL workflows. You'll work alongside a team that ships fast & sweats reliability and the researchers behind DeepSpeed. We're looking for an engineer who thrives in the ML infrastructure layer and brings a solid understanding of LLMs and post-training to help us scale and grow it. YOU WILL: Design and build across the full stack — from the public training APIs and SDK through the control plane to the GPU data plane. Scale the distributed systems that make GPU compute serverless — multi-tenant scheduling, placement, and capacity-aware routing across regional G

KubernetesAIGoRust
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📍 San Francisco, California, United States· Full-time
✓ High-confidence listingCompany trend -83.9%
Quick readStrong listing-quality and freshness signals

About the Team The Emerging Products team is a lean, high-output product lab group that builds products at the forefront of model capabilities. We collaborate across all teams within the company, from research and infrastructure to consumer products. The team is responsible for identifying new product opportunities, building them quickly, dogfooding them internally, and then launching the successful products to users. We use data, user research, and analytics to inform our ideas, and make decisions on what experiments are worth iterating, stopping, or scaling. About the Role We’re looking for a senior, product-minded software engineer to own ambiguous 0-to-1 work from idea through prototype, validation, and handoff. This is a full-stack role with a strong frontend and product emphasis: you will build the interfaces and supporting backend systems needed to test new experiences quickly, while making sound architectural choices that enable successful concepts to scale. This role is based in our Mission Bay office in San Francisco. In this role, you will: Build and ship high-quality, product experiments across the full stack. Turn ambiguous user needs and emerging technical capabilities into testable product concepts, using research and metrics to guide iteration. Own technical direction for 0-to-1 projects, balancing speed, reliability, and a clear path from prototype to scalable product. Partner closely with design, product, research, and engineering teams to dogfood, evaluate, launch, and transition successful experiments. You might thrive in this role if you: Have a track record of building and shipping end-to-end products in fast-moving, startup, founder-led, growth, or other high-ownership environments. Bring strong frontend engineering skills and enough backend and systems depth to make sound full-stack architectural decisions. Pair product intuition with evidence, using user research and product data to identify opportunities and make pragmatic tradeoffs. Operat

AWSRestAIRust
O
📍 San Francisco, California, United States· Full-time
✓ High-confidence listingCompany trend -83.9%
Quick readStrong listing-quality and freshness signals

About the Team Our Robotics team is focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings. Working across the entire model stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the constraints of physical systems to improve peoples’ lives. About the Role We’re looking for a Robotics Control Systems Engineer to take on a foundational role within our robotics team. You’ll help architect, implement, tune, and verify the control infrastructure that enables intelligent, reliable, and responsive robot behavior. This is a deeply hands-on role focused on real-time systems, actuation, dynamics, low-level hardware interaction, and whole-robot performance. You’ll spend significant time working directly with robots onsite: debugging behavior, tuning subsystems, running experiments, and providing feedback across mechanical, electrical, and software teams. This role is based in San Francisco, CA, and requires in-person 5 days a week. In this role, you will: Design and implement real-time control algorithms for robotic systems, including motion control, feedback loops, state estimation, actuator control, and subsystem tuning. Define the control architecture from low-level actuators and hardware interfaces through whole-robot behavior and policy. Identify and characterize actuator, hardware, and software parameters through rigorous experimentation, testing, commissioning, and verification. Work with machine learning engineers to implement reinforcement learning models. Collaborate across mechanical, electrical, and software teams to integrate control logic with sensing and actuation hardware. Help inform the mechanical and electrical design to maximize capability and flexibility. Create the control system architecture; determine the correct level of abstraction from actuators all the way up to whole-robot

AWSRestMachine LearningAI
O
📍 San Francisco, California, United States· Full-time· Remote
✓ Quality checkedCompany trend -83.9%

About the Team The Product Explorations team is a small, high-ownership product exploration group focused on turning new model capabilities into impactful user experiences. We operate across Applied and Research, working closely with product, engineering, design, and research partners to identify promising opportunities, prototype quickly, validate ideas, and determine which efforts should scale into larger product investments. Our objective is broad: discover and build experiences that can meaningfully improve user engagement, product impact, and revenue across OpenAI’s products. We move quickly, follow strong product signals, and take ideas from early exploration to meaningful user impact. About the Role We are looking for product-minded full stack engineers to join the Product Explorations team and drive high-impact, net-new product explorations. In this role, you will independently identify promising opportunities, move quickly from idea to prototype, and work across teams to validate and launch new experiences. You will operate with a high degree of autonomy, use strong product intuition, and make thoughtful decisions in ambiguous environments. You may work on a new ChatGPT experience one week, explore a new model capability the next, and partner with Research or Applied teams to turn the strongest ideas into scalable products. This role is a strong fit for someone who enjoys building from scratch, iterating quickly, and working on projects where the path forward is not always obvious. In this role, you will: Identify and pursue high-leverage product and technical opportunities across OpenAI’s products Move quickly from idea to prototype, validation, launch, and iteration Build full stack product experiences that unlock value from new model capabilities Work closely with Research, Applied, product, design, data science, and engineering partners Use user feedback, product signals, and experimentation to determine which ideas should scale Drive alignment across t

AWSRestAIRust
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📍 Santa Clara, United States
✓ Quality checkedCompany trend -12.7%

We are looking for a Senior System Software Engineer, Software Defined Networking to design, build, and operate highly performant and scalable SDN solutions for NVIDIA's AI Clouds hosting GPU-accelerated workloads — including hyperscale multi-node training, inference, cloud gaming, and cloud functions. This role spans the full lifecycle of our SDN stack — from designing and developing new control and data plane software to ensuring operational excellence in production through reliability engineering, CI/CD, observability, and incident response. What you'll be doing: Design and develop next-generation multi-tenant cloud SDN control and data plane software (OVS, OVN, OpenFlow) Build Infrastructure-as-a-Service virtual network orchestration and services using gRPC and REST to support tenant workload security and performance SLAs for BMaaS, VMaaS, and Kubernetes Drive upstream contributions to OVN-Kubernetes and related open-source projects Develop software for network observability — monitoring, telemetry, intelligent metering, and performance analysis Operate and support OVS-OVN based SDN solutions in large-scale NVIDIA AI Cloud environments Own end-to-end observability for the SDN stack — build and maintain monitoring, alerting, distributed tracing, and dashboarding to ensure real-time insight into network health, performance, and tenant SLAs Design, enhance, and maintain CI/CD pipelines (GitLab) across Linux host networking, OVS, OVN, and Kubernetes CNIs Implement GitOps approaches or related experience for secure, seamless integration with cloud infrastructure Drive reliability through incident management, resource monitoring, and performance tuning<

PythonAWSAzureGCP
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📍 Santa Clara, United States
✓ Quality checkedCompany trend -12.7%

NVIDIA is seeking a Senior Software Engineer to help us develop distributed storage services for AI/ML. In this role you will work closely with the broader NVIDIA team to design and build a reliable, scalable, and efficient storage-as-a-service tailored to AI applications that can be deployed anywhere and scale without limitations. This service supports the whole NVIDIA critical business from graphics drivers to autonomous vehicles to deep learning frameworks. To achieve this goal, we are looking for an engineer with a deep understanding of distributed systems, outstanding design skills, and a track record in building and delivering large-scale distributed services. What you will be doing: Leading the overall architecture and design of our distributed storage service optimized for AI/ML Develop and maintain distributed, robust and scalable Go programs deployed to state of the art open-source ecosystems, including Kubernetes. Develop and maintain user-space applications, containers, Go-bindings, and CLI tools. Building features for a distributed storage service to enhance availability and reliability for large-scale deployments Engaging and collaborating with NVIDIA Research, Computing, Product teams, cross-functional teams, and external customers to deliver Cloud services. Automating distributed storage service end-to-end, including deployment, management, and monitoring What we need to see: Bachelor’s of Science in Computer Science, or related field (or equivalent experience) with 8&#43; years of industry experience Strong background in developing distributed systems involving Golang, Kubernetes, and Cloud Service Provider integrations Strong track record of delivering distributed services in a variety of distributed computing environments Experience in i

KubernetesArtificial IntelligenceAIGolang
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📍 Santa Clara, United States
✓ Quality checkedCompany trend -12.7%

NVIDIA has transformed computer graphics, PC gaming, and accelerated computing for more than 25 years through exceptional technology and the people who build it. In semiconductor manufacturing, our role is to enable the ecosystem, not compete within it. We partner with fabs, equipment manufacturers, and software providers to make inspection, metrology, and manufacturing intelligence dramatically faster on the NVIDIA platform. Our team builds the software that makes this possible: models, adaptation and evaluation workflows, and deployable inference capabilities that partners integrate into their own tools. We work in environments where labeled data is limited and proprietary, distributions shift across tools and fabs, production budgets are tight, and software must operate inside air-gapped facilities. We’re seeking a Principal Systems Software Engineer for Semiconductor Inspection in Santa Clara. This is a hands-on architect role: you will define the approach, build it, evaluate it, and demonstrate the results. You will work across computer vision, time-series modeling, multimodal AI, anomaly detection, model adaptation, evaluation, and production inference. Success means technology that a fab or equipment vendor can integrate, operate, and trust—not only a successful internal demonstration. What you’ll be doing: Define and prototype AI system architectures spanning optical and e-beam inspection, wafer and mask inspection, metrology, defect review, equipment signals, and process data. Advance world foundation model capabilities for semiconductor manufacturing, including vision, time-series and multimodal representation learning, model adaptation, domain transfer, and data-scarce defect understanding. Develop workflows for defect detection, classification, localization, segmentation, nuisance filtering, ADC, AD

PythonMachine LearningAI
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