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Adversarial Machine Learning Jobs in Fremont, CA

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Machine Learning Engineer - Search Ads

San Jose, CA · On-site

$162K - $316K/yr

... adversarial engineering, and online advertising systems. • Good knowledge in one of the following ... machine learning, deep learning, backend, large-scale systems, data science, full-stack. Job ...

Anthropic

Red Team Engineer, Safeguards

San Francisco, CA · On-site +1

$320K - $405K/yr

Experience with AI/ML security or adversarial machine learning * Understanding of AI safety considerations beyond traditional security, including modern guardrails against jailbreaks * Experience ...

Doist

Red Team Engineer, Safeguards

San Francisco, CA · On-site

$320 - $405/hr

Experience with AI/ML security or adversarial machine learning * Understanding of AI safety considerations beyond traditional security, including modern guardrails against jailbreaks * Experience ...

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Adversarial Machine Learning information

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How much do adversarial machine learning jobs pay per hour?

As of Sep 6, 2026, the average hourly pay for adversarial machine learning in Fremont, CA is $23.35, according to ZipRecruiter salary data. Most workers in this role earn between $20.53 and $25.00 per hour, depending on experience, location, and employer.

What is adversarial machine learning?

Adversarial machine learning is a field of study focused on understanding and defending against attacks that manipulate machine learning models by feeding them deceptive input, known as adversarial examples. These attacks can cause models to make incorrect predictions, raising concerns about the security and reliability of AI systems, especially in critical applications like image recognition and autonomous vehicles. Researchers in this area develop techniques to detect, prevent, and mitigate these vulnerabilities to make machine learning systems more robust.

What are some common challenges faced by professionals working in adversarial machine learning roles?

Adversarial Machine Learning professionals often face the challenge of staying ahead of rapidly evolving attack techniques that can compromise model integrity and security. Managing the balance between model performance and robustness is another key difficulty, as defenses against adversarial attacks can sometimes reduce accuracy or increase computational costs. Collaboration with data scientists, security teams, and software engineers is vital for developing resilient models and implementing effective defenses. Staying current with the latest research and tools is essential for success in this dynamic field.

What are the key skills and qualifications needed to thrive as an adversarial machine learning specialist, and why are they important?

To excel in Adversarial Machine Learning, you need a strong background in machine learning, deep learning, statistics, and computer science, typically supported by an advanced degree in a related field. Familiarity with frameworks like TensorFlow or PyTorch, experience with adversarial attack and defense libraries, and knowledge of security protocols are crucial. Creative problem-solving, critical thinking, and strong communication skills help in designing robust models and explaining complex threats to stakeholders. These competencies are vital to anticipate vulnerabilities, safeguard AI systems, and ensure the reliability of machine learning models in real-world applications.

What is the difference between Adversarial Machine Learning vs Data Scientist?

AspectAdversarial Machine LearningData Scientist
CredentialsKnowledge of machine learning, cybersecurity, and threat detectionDegree in data science, statistics, or related fields
Work EnvironmentResearch labs, cybersecurity teams, AI developmentBusiness analytics, data analysis, model development
Industry UsageAI security, cybersecurity, machine learning researchBusiness, finance, healthcare, tech companies

Adversarial Machine Learning focuses on understanding and defending AI models against malicious inputs, often within cybersecurity contexts. Data Scientists analyze data to extract insights, build models, and support decision-making across various industries. While both roles require machine learning knowledge, Adversarial Machine Learning emphasizes security and robustness, whereas Data Scientists focus on data analysis and predictive modeling.

What are popular job titles related to Adversarial Machine Learning jobs in Fremont, CA?

For Adversarial Machine Learning jobs in Fremont, CA, the most frequently searched job titles are:

What job categories do people searching Adversarial Machine Learning jobs in Fremont, CA look for?

The top searched job categories for Adversarial Machine Learning jobs in Fremont, CA are:

What cities near Fremont, CA are hiring for Adversarial Machine Learning jobs?

Cities near Fremont, CA with the most Adversarial Machine Learning job openings:

Adversarial Machine Learning jobs near you
Infographic showing various Adversarial Machine Learning job openings in Fremont, CA as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 24% Part Time, 1% Temporary, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $48,563 per year, or $23.3 per hour.

Staff AI Machine Learning Engineer

Medeloop

San Francisco, CA

Full-time

Re-posted 16 days ago

Job description

The Role

We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI — and a true passion for experimentation and creation — to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop's technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem. 

What You'll Own
  • Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A).
  • Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows).
  • Develop rigorous evaluation and safety frameworks — automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production.
  • Drive LLM and ML model development — train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges.
  • Shape Medeloop's agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership.
  • Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI.
What We're Looking For
  • 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains.
  • Experience working on analytic engines (or advanced analytics platforms) — designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale.
  • Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms.
  • Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix).
  • Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors.
  • A builder/experimenter mindset — you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems.
  • Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats.
Bonus Points
  • Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility).
  • Multi-cloud experience (AWS, Azure, GCP)
Why Medeloop
  • Ownership from day one: small team, high-trust, no layers between your work and its impact
  • Technically ambitious: you'll build AI-powered workflows, not just support them
  • Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter
  • Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build