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Machine Learning Engineer - Intelligent Agents & Systems at Zyphra Technologies Inc.

Zyphra Technologies Inc. No longer available

Job Description

Zyphra is an artificial intelligence company based in Palo Alto, CaliforniaThe 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

Typical mid-level pay: $141k for Computer and Information Research Scientists nationally

National salary averages
Expected mid-level
$141k
Entry
Mid
Senior
Expected
$81k Market range (10th-90th percentile) $232k

Senior roles pay 76% more than entry—experience is well rewarded.

Balanced market

High demand and responsive wages. Negotiate confidently on all fronts.

Hiring leverage
Lean candidate
Wage leverage
Moderate
Mobility
Low mobility

Who this leverage applies to

Stronger for: All experience levels, Credentialed candidates
Weaker for: Self-taught practitioners

Where to negotiate

Base salary
Sign-on bonus
Title / level
Remote flexibility
Scope & responsibility
Start date / PTO

Likely Possible Unlikely

Watch out for

Limited mobility: Few adjacent roles—switching employers is harder.

Does this path compound?

Job Growth →
High churn
Growth, flat pay
🚀 Compound
Growth + pay upside
⚠️ Plateau
Limited growth
Specialize
Experts earn more
Pay Upside →
Growth + pay upside

Both the field and your earnings can grow significantly.

+20%
10yr growth
Advanced degrees are common in this field.
Typical: Master's degree

Good time to build expertise—demand will chase supply.

Labor data: BLS 2024