Zyphra Technologies Inc.
Palo Alto, California
Zyphra is an artificial intelligence company based in Palo Alto, California The Role: As a Machine Learning Engineer , you will be a core contributor to Zyphra's Agentic Systems and Interaction projects. You will be at the forefront of building a next-generation desktop and browser-based agent that can autonomously navigate the web, interact with filesystems, and complete complex user tasks. This role spans frontend interfaces, secure sandboxing environments, large-scale document search and retrieval, and language/vision model integration. You'll work across: Design and implementation of an agentic system capable of interacting with browsers, operating systems, and enterprise filesystems Building search and retrieval pipelines across large-scale structured and unstructured data Integrating LLMs, vision models, reinforcement learning, and scaffolding frameworks for autonomous, multi-step decision-making Engineering secure virtualized runtimes and backend services for agent execution What matters most is your drive to build production-grade ML systems that push the boundary of what software agents can do We value velocity and curiosity, especially in fast-moving and ambiguous environments Requirements: Proficiency in Python and a deep understanding of building and debugging complex ML-driven applications Experience working with desktop operating systems (Windows and macOS), including APIs for screen reading, file interaction, and accessibility frameworks Experience developing browser extensions or automation tools with fine-grained control over the browser (mouse, tabs, DOM) Understanding of LLMs, prompting techniques, and orchestration frameworks for multi-step reasoning Ability to work across the full ML stack, from model integration to serving infrastructure Experience designing or working with secure and virtualized execution environments Excellent communication and collaboration skills across product, research, and engineering teams Bonus Qualifications: Experience building or integrating retrieval-augmented generation (RAG) systems Experience working with enterprise security and compliance frameworks (e.g., SOC 2) Familiarity with vector databases and large-scale document indexing Knowledge of web automation tools and headless browser environments (e.g, Puppeteer, Playwright) Understanding of sandboxed or containerized compute environments with strict access controls Comfort designing user-facing agentic workflows and reasoning systems that span multiple modalities (text, vision, actions) Experience using and fine-tuning models for screen reading, OCR, or UI understanding Background in HCI or interest in building intuitive agent interfaces that extend human capabilities Why Work at Zyphra: Our research methodology is to make grounded, methodical steps toward ambitious goals. Both deep research and engineering excellence are equally valued We strongly value new and crazy ideas and are very willing to bet big on new ideas We move as quickly as we can; we aim to minimize the bar to impact as low as possible We all enjoy what we do and love discussing AI Benefits and Perks: Comprehensive medical, dental, vision, and FSA plans Competitive compensation and 401(k) Relocation and immigration support on a case-by-case basis On-site meals prepared by a dedicated culinary team; Thursday Happy Hours In-person team in Palo Alto, CA, with a collaborative, high-energy environment
Zyphra is an artificial intelligence company based in Palo Alto, California The Role: As a Machine Learning Engineer , you will be a core contributor to Zyphra's Agentic Systems and Interaction projects. You will be at the forefront of building a next-generation desktop and browser-based agent that can autonomously navigate the web, interact with filesystems, and complete complex user tasks. This role spans frontend interfaces, secure sandboxing environments, large-scale document search and retrieval, and language/vision model integration. You'll work across: Design and implementation of an agentic system capable of interacting with browsers, operating systems, and enterprise filesystems Building search and retrieval pipelines across large-scale structured and unstructured data Integrating LLMs, vision models, reinforcement learning, and scaffolding frameworks for autonomous, multi-step decision-making Engineering secure virtualized runtimes and backend services for agent execution What matters most is your drive to build production-grade ML systems that push the boundary of what software agents can do We value velocity and curiosity, especially in fast-moving and ambiguous environments Requirements: Proficiency in Python and a deep understanding of building and debugging complex ML-driven applications Experience working with desktop operating systems (Windows and macOS), including APIs for screen reading, file interaction, and accessibility frameworks Experience developing browser extensions or automation tools with fine-grained control over the browser (mouse, tabs, DOM) Understanding of LLMs, prompting techniques, and orchestration frameworks for multi-step reasoning Ability to work across the full ML stack, from model integration to serving infrastructure Experience designing or working with secure and virtualized execution environments Excellent communication and collaboration skills across product, research, and engineering teams Bonus Qualifications: Experience building or integrating retrieval-augmented generation (RAG) systems Experience working with enterprise security and compliance frameworks (e.g., SOC 2) Familiarity with vector databases and large-scale document indexing Knowledge of web automation tools and headless browser environments (e.g, Puppeteer, Playwright) Understanding of sandboxed or containerized compute environments with strict access controls Comfort designing user-facing agentic workflows and reasoning systems that span multiple modalities (text, vision, actions) Experience using and fine-tuning models for screen reading, OCR, or UI understanding Background in HCI or interest in building intuitive agent interfaces that extend human capabilities Why Work at Zyphra: Our research methodology is to make grounded, methodical steps toward ambitious goals. Both deep research and engineering excellence are equally valued We strongly value new and crazy ideas and are very willing to bet big on new ideas We move as quickly as we can; we aim to minimize the bar to impact as low as possible We all enjoy what we do and love discussing AI Benefits and Perks: Comprehensive medical, dental, vision, and FSA plans Competitive compensation and 401(k) Relocation and immigration support on a case-by-case basis On-site meals prepared by a dedicated culinary team; Thursday Happy Hours In-person team in Palo Alto, CA, with a collaborative, high-energy environment
Zyphra Technologies Inc.
Palo Alto, California
Zyphra is an artificial intelligence company based in Palo Alto, California. The Role: As a Machine Learning Data Engineer - Systems & Retrieval, you will build and optimize the data infrastructure that fuels our machine learning systems. This includes designing high-performance pipelines for collecting, transforming, indexing, and serving massive, heterogeneous datasets from raw web-scale data to enterprise document corpora. You'll play a central role in architecting retrieval systems for LLMs and enabling scalable training and inference with clean, accessible, and secure data. You'll have an impact across both research and product teams by shaping the foundation upon which intelligent systems are trained, retrieved, and reasoned over. You'll work across: Design and implementation of distributed data ingestion and transformation pipelines Building retrieval and indexing systems that support RAG and other LLM-based methods Mining and organizing large unstructured datasets, both in research and production environments Collaborating with ML engineers, systems engineers, and DevOps to scale pipelines and observability Ensuring compliance and access control in data handling, with security and auditability in mind Requirements: Strong software engineering background with fluency in Python Experience designing, building, and maintaining data pipelines in production environments Deep understanding of data structures, storage formats, and distributed data systems Familiarity with indexing and retrieval techniques for large-scale document corpora Understanding of database systems (SQL and NoSQL), their internals, and performance characteristics Strong attention to security, access controls, and compliance best practices (e.g., GDPR, SOC2) Excellent debugging, observability, and logging practices to support reliability at scale Strong communication skills and experience collaborating across ML, infra, and product teams Bonus Skill Set: Experience building or maintaining LLM-integrated retrieval systems (e.g, RAG pipelines) Academic or industry background in data mining, search, recommendation systems, or IR literature Experience with large-scale ETL systems and tools like Apache Beam, Spark, or similar Familiarity with vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding-based retrieval Understanding of data validation and quality assurance in machine learning workflows Experience working on cross-functional infra and MLOps teams Knowledge of how data infrastructure supports training pipelines, inference serving, and feedback loops Comfort working across raw, unstructured data, structured databases, and model-ready formats Why Work at Zyphra: Our research methodology is to make grounded, methodical steps toward ambitious goals. Both deep research and engineering excellence are equally valued We strongly value new and crazy ideas and are very willing to bet big on new ideas We move as quickly as we can; we aim to minimize the bar to impact as low as possible We all enjoy what we do and love discussing AI Benefits and Perks: Comprehensive medical, dental, vision, and FSA plans Competitive compensation and 401(k) Relocation and immigration support on a case-by-case basis On-site meals prepared by a dedicated culinary team; Thursday Happy Hours In-person team in Palo Alto, CA, with a collaborative, high-energy environment If you're excited by the challenge of high-scale, high-performance data engineering in the context of cutting-edge AI, you'll thrive in this role. Apply Today!
Zyphra is an artificial intelligence company based in Palo Alto, California. The Role: As a Machine Learning Data Engineer - Systems & Retrieval, you will build and optimize the data infrastructure that fuels our machine learning systems. This includes designing high-performance pipelines for collecting, transforming, indexing, and serving massive, heterogeneous datasets from raw web-scale data to enterprise document corpora. You'll play a central role in architecting retrieval systems for LLMs and enabling scalable training and inference with clean, accessible, and secure data. You'll have an impact across both research and product teams by shaping the foundation upon which intelligent systems are trained, retrieved, and reasoned over. You'll work across: Design and implementation of distributed data ingestion and transformation pipelines Building retrieval and indexing systems that support RAG and other LLM-based methods Mining and organizing large unstructured datasets, both in research and production environments Collaborating with ML engineers, systems engineers, and DevOps to scale pipelines and observability Ensuring compliance and access control in data handling, with security and auditability in mind Requirements: Strong software engineering background with fluency in Python Experience designing, building, and maintaining data pipelines in production environments Deep understanding of data structures, storage formats, and distributed data systems Familiarity with indexing and retrieval techniques for large-scale document corpora Understanding of database systems (SQL and NoSQL), their internals, and performance characteristics Strong attention to security, access controls, and compliance best practices (e.g., GDPR, SOC2) Excellent debugging, observability, and logging practices to support reliability at scale Strong communication skills and experience collaborating across ML, infra, and product teams Bonus Skill Set: Experience building or maintaining LLM-integrated retrieval systems (e.g, RAG pipelines) Academic or industry background in data mining, search, recommendation systems, or IR literature Experience with large-scale ETL systems and tools like Apache Beam, Spark, or similar Familiarity with vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding-based retrieval Understanding of data validation and quality assurance in machine learning workflows Experience working on cross-functional infra and MLOps teams Knowledge of how data infrastructure supports training pipelines, inference serving, and feedback loops Comfort working across raw, unstructured data, structured databases, and model-ready formats Why Work at Zyphra: Our research methodology is to make grounded, methodical steps toward ambitious goals. Both deep research and engineering excellence are equally valued We strongly value new and crazy ideas and are very willing to bet big on new ideas We move as quickly as we can; we aim to minimize the bar to impact as low as possible We all enjoy what we do and love discussing AI Benefits and Perks: Comprehensive medical, dental, vision, and FSA plans Competitive compensation and 401(k) Relocation and immigration support on a case-by-case basis On-site meals prepared by a dedicated culinary team; Thursday Happy Hours In-person team in Palo Alto, CA, with a collaborative, high-energy environment If you're excited by the challenge of high-scale, high-performance data engineering in the context of cutting-edge AI, you'll thrive in this role. Apply Today!