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Fetch Ai Jobs in California (NOW HIRING)

Globalfoundries

Staff Engineer AI/ML

Santa Clara, CA · On-site

$143K - $275K/yr

Responsible for Defining, leading and owning RTL development of our latest AI-enabled RISC-V CPU ... Instruction fetch and decode, branch prediction techniques * Instruction scheduling, register ...

GlobalFoundries

Staff Engineer AI/ML

Santa Clara, CA · On-site

$143K - $275K/yr

Responsible for Defining, leading and owning RTL development of our latest AI-enabled RISC-V CPU ... Instruction fetch and decode, branch prediction techniques * Instruction scheduling, register ...

Velaura

Design Verification Engineer- CPU

Santa Clara, CA · On-site

$200K - $350K/yr

... Physical AI SoC. You will work closely with architects, RTL engineers, software engineers and ... Own verification of custom CPU units including instruction fetch, decode, load store, and the ...

Velaura

Design Verification Engineer- CPU

Santa Clara, CA · On-site

$200K - $350K/yr

... Physical AI SoC. You will work closely with architects, RTL engineers, software engineers and ... Own verification of custom CPU units including instruction fetch, decode, load store, and the ...

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$128K

$175.2K

How much do fetch ai jobs pay per year?

As of Aug 31, 2026, the average yearly pay for fetch ai in California is $128,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,000.00 and $135,700.00 per year, depending on experience, location, and employer.

What is a Fetch AI?

A Fetch.ai job typically involves working with decentralized artificial intelligence, blockchain technology, and autonomous agents to build smart, self-learning systems. Roles may include software engineering, machine learning, blockchain development, or research positions focused on AI-driven automation. Employees work on optimizing supply chains, financial markets, and IoT applications through AI and blockchain integration. Skills in Python, smart contracts, and distributed ledger technology are often required. Fetch.ai jobs aim to create autonomous systems that enhance efficiency and decision-making across industries.

What does a Fetch AI engineer do?

As a Fetch.ai Engineer, your daily responsibilities typically include designing, developing, and deploying intelligent agent-based solutions, working with blockchain protocols, and maintaining high-quality, scalable code. You will collaborate closely with data scientists, product managers, and other engineers to ensure project requirements align with real-world solutions. One common challenge is staying current with rapidly evolving AI and blockchain technologies while solving complex problems around system scalability and security. The environment is fast-paced and innovative, offering opportunities to work on cutting-edge projects that have tangible industry impact.

What skills and qualifications are needed to thrive as a Fetch AI engineer?

To excel in a Fetch.ai Engineer role, you need a solid background in computer science, machine learning, and distributed systems, often supported by a relevant degree and proven software development experience. Familiarity with Python, blockchain technologies, multi-agent systems, and cloud platforms is highly valued, along with certifications in AI or blockchain. Strong problem-solving, collaboration, and communication skills are essential to work effectively within multidisciplinary teams and on complex, evolving projects. These competencies are crucial for developing robust, innovative solutions that advance the Fetch.ai platform and enable seamless integration in real-world applications.

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Fetch Ai jobs near you
Infographic showing various Fetch Ai job openings in California as of August 2026, with employment types broken down into 77% Full Time, 20% Part Time, and 3% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution, with an average salary of $128,018 per year, or $61.5 per hour.

AI Context & Data Infrastructure Engineer

Town.com, Inc

San Francisco, CA • On-site

$250K - $300K/yr

Full-time

Posted 2 days ago

New

Job description

About Town
Town (town.com) is AI that starts from who you are. We build a persistent model of your identity, your voice, your judgment, your relationships, and your priorities, and use it to do real work on your behalf across every tool where you operate: email, calendar, documents, Slack, and more. Town doesn't wait for you to prompt it. It observes, learns, and acts. The more you use it, the more it becomes an extension of you.
Town was founded by Jean-Denis Greze (CEO), former CTO of Plaid, and Tony Vincent (CPO), former Director of Applied AI Product at Google. We're a small, talent-dense team backed by Andreessen Horowitz, Forerunner Ventures, First Round Capital, and Conviction, with more than $73M raised to date.
About the role
Town is building the most personalized, most capable AI assistant for everyone - and personalization at that level is a retrieval and data problem. The assistant is only as good as the context it can bring into the moment: the right memory, message, document, or relationship, pulled fast and related by meaning across everything a person and their team touch.
You'll build the foundation the whole product reasons over: the search and data infrastructure behind that context. One shared retrieval layer combining lexical and semantic search, the realtime and batch pipelines that keep it fresh and correct, and the durable data model everything else is built on.
This is greenfield and high-leverage: you'll be the first person building this layer.
What you'll do
  • Build the search and retrieval layer that puts the right context at every Townie's fingertips, the moment it's needed - one shared layer the whole product pulls from instead of refetching context on its own.
  • Combine lexical and semantic search and own the tradeoffs between them: vector vs. lexical, precompute vs. fetch, hybrid retrieval, and ranking.
  • Design the durable data model the assistant's work is built on, so context is relevant, fast, and cost-effective.
  • Build and operate the pipelines behind it - realtime/streaming and batch - that keep the index fresh and correct as the underlying data changes.
  • Stand up the indexing and storage layer and keep it fast and reliable at scale: latency, cost, freshness, and completeness.
  • Lay the groundwork for relating content by meaning across everything the assistant knows - the start of a knowledge graph of people, companies, projects, and how they connect.

You might thrive here if you...
  • Have significant, hands-on experience across lexical and semantic search components and approaches (BM25, embeddings, ANN/vector indexes, hybrid retrieval, ranking).
  • Have run large-scale data infrastructure, ideally both realtime/streaming and batch - pipelines, indexing, and storage.
  • Can make retrieval fast and cheap at scale, and reason about the latency, cost, and freshness tradeoffs cold.
  • Are a systems thinker comfortable in greenfield, where the foundation doesn't exist yet.
  • Are excited to take these systems from rapid prototype to production scale.
  • Bonus if you've worked on ranking/relevance, knowledge graphs, or retrieval for LLM or agentic systems.

Location
San Francisco, CA. Five days a week in person at our Financial District office.

Apply