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We are looking for an infrastructure leader with a strong software engineering background.
As an infrastructure leader in the Cloud-Scale Machine Learning Acceleration team, youll be responsible for building testing infrastructure for the development of Trainium chips.

Key job responsibilities:
1. Design a framework to automate verification and validation tests for pre-silicon and silicon on multiple platforms (simulation, emulation, silicon prototypes, etc.).
2. Implement software for different types of environments (embedded / x86).
3. Build systems to collect and analyze test results and related test artifacts like logs.
4. Build visual tracking tools for regression and coverage metrics.
5. Drive integration in multiple teams and ensure the automation system scales with org needs.

As a Front End Engineer, you will solve technical challenges and act as a mentor to other junior engineers. Given that this is an early-stage initiative, you'll play an active role in translating business and functional requirements into concrete deliverables and build quick prototypes or proofs of concept in partnership with other technology leaders within the team. You will help invent new features, design, develop and deploy highly scalable and reliable distributed services. You will work with a variety of core languages (primarily React and typescript) and technologies (AWS). You will work closely with Product Managers, Engineers and Applied Scientists.

You will have the exciting opportunity to be on the ground floor of developing this cutting-edge product. We are seeking creative problem-solvers who are energized by tackling complex technical challenges to push the boundaries of what's possible with AI. Our team collaborates closely across roles to rapidly build, test and iterate as we create an AI solution that delivers real business value. Join our innovative team to leave your mark on the next generation of AI.

We are looking for talented Engineers to join our Virtual Platforms team.
Virtual platforms play a crucial role in accelerating the development process by enabling early Software development, System-Level testing, and architectural exploration without the constraints of physical prototypes.

As a Virtual Platform Engineer, you will:
Develop accurate and efficient simulation models of hardware components.
Create and maintain Virtual Platform environments for software development and system-level validation.
Debug and optimize complex hardware-software interactions.

We're Hiring: Senior/Technical Leader/VP of Engineering and AI to lead a dev team with deep experience in
Distributed systems and architecture design
DevOps infrastructure
AI/ML, LLMs, MLOps
Scaling systems (cloud and on-prem)
We're building Argu.ai atop tier startup that's backed by the world's top 3 VCs
and redefines how machines understand video and real-world environments.
We're looking for a super-star technical leader. HandsOn by nature, but with leadership skills. This is a chance to lead and build systems that power some of the most known and high-profile places on earth, handle millions of users.
What You'll Own
Architect, build, and scale on-prem and cloud distributed systems
Lead infrastructure design and building for video streaming at scale (RTSP, WebRTC, HLS)
Build systems that support insanely high data traffic, especially streaming data.

We are seeking for System Integration Engineer to help define, shape, and integrate solutions for the next generation of our cloud platforms. As System Integration Engineer youll join Annapurnas product system team, which develops advanced technologies used across our data centers, supporting services like Nitro and Graviton.
You will take ownership of end-to-end system interfaces (e.g., PCIe, Ethernet, DDR) throughout the product lifecycle, from concept to post-deployment debugging.

Key job responsibilities:
1. Setting interface design strategies.
2. Ensuring optimal design implementation.
3. Driving complex system debugs involving hardware, firmware, and software components.
4. Providing leadership to raise quality standards.

A Research Engineer supports Applied Scientists in the team by helping to build tools, experiment frameworks, and infrastructures. A Research Engineer may be called to directly facilitate science benchmarking, and deep dive on any deep learning related technical problems. He/she may be called to lead or facilitate a joint project between science and engineering team.

Key job responsibilities:
* Participate research and development: direct or indirectly contribute to developing latest Retrieval Augmented Generation technologies, fine-tuning techniques for embedding models and LLMs.
* Tool development: build and improve toolings that applied scientists would need, including scalable science experiment frameworks, low-latency testing pipelines, data collection/annotation/evaluation web applications.
* Expedite science to production: bridge the gap between science and engineering teams, help to investigate and mitigate gaps between science and engineering pipeline. Help to merge both pipelines to one, or making components within the two interchangeable. Work towards a fast science-to-production paradigm.
* Science code quality control: serve as gate keeper, review and hold science code quality to production standard.
* Manage science infrastructure: act as admin to manage all science AWS account, including patch any security risks, build templates, AWS Cops, conduct cost reduction operations.