Applied Ai Engineer, Silicon Engineering
etched
OnSite
San Jose
AI
Job Score
80 pts
On-site model (+70)
AI (+10)
Applied AI Engineer, Silicon Engineering
About Etched
Etched is building AI chips that are hard-coded for individual model architectures. Our first product (Sohu) only supports transformers, but has an order of magnitude more throughput and lower latency than a B200. With Etched ASICs, you can build products that would be impossible with GPUs, like real-time video generation models and extremely deep & parallel chain-of-thought reasoning agents.
Job Summary
We are using AI to build AI chips. AI agents are starting to genuinely work for verification, debug, and EDA flows — we want someone to bring that inside Etched and push past it. As an Applied AI Engineer, you will embed with our hardware teams — RTL design, verification, DFT, physical design, and silicon validation — and build the agents and tooling that multiply their output. You'll wire LLM agents into simulators, regressions, waveform and log analysis, EDA flows, and bring-up workflows, and own the evals that separate demos from tools engineers actually rely on. This is an internal, force-multiplier role: your success is measured by how much faster the chip team moves, not by lines of code you ship yourself. It is not a customer-facing role and not about inference serving — it's AI applied to how we build the chip itself. You do not need to be a chip designer or a traditional software engineer — you need to be an exceptional problem solver who has shipped real agentic systems, works comfortably across stacks and domains, and uses AI to ramp on hard new problems fast.
Key responsibilities
Build, deploy, and maintain LLM-agent workflows that accelerate chip development: debug triage, testbench and coverage work, log/waveform analysis, EDA script generation, and engineering knowledge retrieval
Embed with hardware teams to find the highest-leverage pain points, then turn them into automated workflows with measurable adoption
Design rigorous evals for agent performance on real silicon-engineering tasks — not proxy metrics — and use them to drive iteration
Integrate agents with our internal infrastructure: simulation and emulation flows, CI/regression systems, lab equipment, and issue tracking, via tool-calling and MCP
Champion adoption: documentation, training, and fast feedback loops with the engineers who use what you build
You may be a good fit if you have
A track record of solving hard problems across stacks and domains — you enjoy being dropped into unfamiliar territory and figuring it out
Comfort with Python and code: you can read it, modify it, debug it, and direct AI to write it well. We do not care whether you write code from scratch — we care whether you ship things that work
Fluency using AI to learn and ramp on new problems — agentic coding tools, deep research, and frontier models are how you work, not an add-on
Hands-on experience building and shipping LLM-based agents or AI tooling that real users depend on (beyond calling an API — context engineering, tool integration, orchestration, failure analysis)
An eval-driven mindset: you measure whether AI systems actually work before scaling them
High agency and comfort with ambiguity — you can find the problem, not just solve the stated one
Interest in chip development and the ability to ramp quickly on a deeply technical domain. Hardware experience is a real plus, but not required — you will be willing and able to learn quickly
Strong candidates may also have experience with
Chip development in any form (the strongest plus): RTL/SystemVerilog, functional verification (UVM), DFT, physical design/STA, FPGA, emulation, or silicon bring-up and validation
EDA tool flows and Tcl scripting; reading waveforms, logs, and regressions
Fine-tuning or post-training (SFT, RLHF/DPO), RAG over proprietary technical data, or multi-agent orchestration
Deep software engineering: C++ or Rust, developer-facing internal platforms, CI/CD at scale, or infrastructure (Docker, Slurm, Ray)
Representative projects
In your first 30 days, pick one hardware team's worst recurring pain, ship an agent for it, and prove adoption with usage data
Build an agent that triages overnight regression failures, clusters them by root cause, and drafts bug reports with waveform and log evidence attached
Wire Claude Code-style agents into our EDA and validation flows via MCP so engineers can drive simulations, queries, and lab equipment from natural language
Create a retrieval system over our specs, design docs, and past debug history that cuts ramp time for new engineers
Design an eval suite that measures agent performance on real verification and debug tasks, and use it to decide which workflows to automate next
Prototype AlphaEvolve-style optimization loops that propose and automatically verify improvements to test programs or flow scripts
Benefits
Full medical, dental, and vision packages, with generous premium coverage
Housing subsidy of $2,000/month for those living within walking distance of the office
Daily lunch and dinner in our office
Relocation support for those moving to San Jose (Santana Row)
Unlimited compute budget subject to ROI justification
How we're different
Etched believes in the Bitter Lesson. We think most of the progress in the AI field has come from using more FLOPs to train and run models, and the best way to get more FLOPs is to build model-specific hardware. Larger and larger training runs encourage companies to consolidate around fewer model architectures, which creates a market for single-model ASICs.
We are a fully in-person team in San Jose (Santana Row), and greatly value engineering skills. We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both as needed.
About Artificial Intelligence
Artificial Intelligence is currently the fastest-growing field in the technology market. The revolution in generative models (GPT, Claude, Gemini) has created massive demand for AI-specialized professionals.
Key areas of practice include Machine Learning Engineering, MLOps, Prompt Engineering, AI Research, and Applied AI. Python, TensorFlow, PyTorch, and LLM knowledge are essential skills.
AI salaries are the highest in the technology sector, with many remote work opportunities at international companies.
Discover Other Areas
Understand the scope of work, key skills, and tools used in different career areas.
About Talent Acquisition
Talent Acquisition is the strategic area responsible for attracting, selecting, and hiring the best professionals for the organization. Unlike traditional recruitment, TA acts as a strategic business partner, aligning talent acquisition with the company's long-term objectives.
Key skills include advanced sourcing, employer branding, labor market analysis, talent pipeline management, and candidate experience. Tools like LinkedIn Recruiter, ATS (Greenhouse, Lever, Ashby), and assessment platforms are essential.
TA professionals in technology companies are highly valued, especially those who master tech sourcing, workforce planning, and recruitment metrics like time-to-hire and cost-per-hire.
About Ecommerce Analyst
The Ecommerce Analyst is the professional responsible for analyzing online sales data, buyer behavior, and virtual store performance to guide strategic decisions. They combine data analysis with ecommerce knowledge to optimize conversion, average order value, and return on investment.
Key skills include Google Analytics (GA4), Hotjar, conversion funnel analysis, cohort analysis, customer segmentation, pricing analysis, and ecommerce metrics (CAC, CLV, AOV, conversion rate). Knowledge of SQL, Power BI, Google Tag Manager, and platforms like Shopify and VTEX is a differentiator.
Ecommerce Analysts in technology companies are highly valued, especially those who can turn buyer behavior data into actionable insights to increase revenue and reduce cart abandonment. The field offers opportunities from junior analyst to ecommerce analytics manager.
About Tech Recruiter
The Tech Recruiter is a professional specialized in recruiting technology talent, from developers to AI engineers and DevOps professionals. They combine technical knowledge with recruitment skills to evaluate and attract highly qualified candidates.
Key skills include technical screening, analysis of technical profiles (GitHub, portfolios, blogs), knowledge of software stacks and architectures, networking in tech communities and events. Proficiency with tools like LinkedIn Recruiter, Gem, Ashby, and technical assessment platforms is a differentiator.
Tech Recruiters are scarce and highly paid professionals, especially those who can map and access passive talent in competitive markets like AI, data engineering, and cloud computing.
About Branding
Branding is the area responsible for building, managing, and strengthening a brand's identity and market value. Branding professionals create strategies that define how the brand is perceived by the public, from the logo to the complete customer experience.
Key skills include brand strategy, visual identity, brand guidelines, positioning, naming, brand voice, market research, brand equity, and brand management. Knowledge of graphic design (Figma, Illustrator, Photoshop), storytelling, and brand experience is a differentiator.
Branding professionals in technology companies are highly valued, especially those who master employer branding, digital branding, and can build strong, memorable brands in competitive markets. The field offers opportunities from brand designer to head of brand, with a focus on identity, differentiation, and perceived value.
About Product Manager
The Product Manager (PM) is the professional responsible for defining the strategy, vision, and roadmap of a digital product. They work at the intersection of technology, business, and user experience (UX), leading the discovery and delivery of solutions that solve real problems in a viable way for the company.
Key skills include product discovery, data and metrics analysis (AARRR, NPS, LTV), user research, go-to-market strategy, roadmapping, strategic prioritization, and leadership by influence. Tools like Amplitude, Mixpanel, Hotjar, Jira, and Notion are fundamental.
Product Managers play a central role in the growth of startups, scale-ups, and large technology companies, with career progression opportunities to Product Leader, Head of Product, and Chief Product Officer (CPO).