ZipRecruiter

Log In

1

Kill Switch Engineer Jobs (NOW HIRING)

Prophecy Technologies

AI Platform Engineer

Dallas, TX · On-site

... kill switches, and graceful degradation. * Expertise in vector search and embeddings at scale ... Strong programming skills in Python 3.11+, FastAPI, async I/O, and Pydantic. * Familiarity with ...

previous pagenext page

Showing results 21-40

All JobsTravel JobsRailroad JobsSwitchman JobsKill Switch Engineer Jobs

Kill Switch Engineer information

What is a kill switch engineer?

A Kill Switch Engineer is responsible for designing, implementing, and maintaining emergency shutdown mechanisms for software, hardware, or network systems. These engineers ensure that critical systems can be safely and efficiently deactivated in case of security threats, software failures, or other emergencies. They work closely with cybersecurity teams, developers, and infrastructure engineers to integrate fail-safe mechanisms that protect data and infrastructure. Their role is crucial in industries like finance, defense, and telecommunications, where system integrity and security are top priorities.

What are the key skills and qualifications needed to thrive as a kill switch engineer?

To thrive as a Kill Switch Engineer, you need a strong background in cybersecurity, network engineering, and system architecture, often supported by a relevant degree such as computer science or information technology. Familiarity with intrusion detection systems (IDS), automated response tools, and certifications like CISSP or CEH is highly valued. Excellent troubleshooting skills, clear communication, and the ability to respond calmly under pressure are key soft skills for this position. These capabilities ensure swift, secure implementation of critical fail-safe mechanisms to protect organizational assets and maintain operational resilience.

What are some common challenges kill switch engineers encounter in their day-to-day roles?

Kill Switch Engineers often face the challenge of designing and maintaining fail-safe mechanisms that must work flawlessly under emergency conditions, requiring robust testing and rapid response capabilities. They regularly collaborate with security teams, network administrators, and IT leadership to ensure the kill switch functions as intended and meets evolving compliance standards. Engineers must also stay proactive in updating protocols to address emerging cyber threats and minimize the risk of accidental triggers. The dynamic nature of the field means adaptability and continuous learning are critical for success.

How much does a Kill Switch Engineer make?

The salary of a Kill Switch Engineer varies depending on experience, location, and industry, but typically ranges from $80,000 to $130,000 annually. Professionals in cybersecurity or software engineering roles with specialized skills in safety systems or emergency shutdown protocols tend to earn higher salaries.
More about Kill Switch Engineer jobs

What cities are hiring for Kill Switch Engineer jobs?

Cities with the most Kill Switch Engineer job openings:

What are the most commonly searched types of Kill Switch Engineer jobs?

The most popular types of Kill Switch Engineer jobs are:

What states have the most Kill Switch Engineer jobs?

States with the most job openings for Kill Switch Engineer jobs include:

What are popular job titles related to Kill Switch Engineer jobs?

For Kill Switch Engineer jobs, the most frequently searched job titles are:

Kill Switch Engineer jobs near you
Infographic showing various Kill Switch Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 92% Full Time, 3% Part Time, and 4% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

Senior Machine Learning Engineer - Agentic AI

Houston, TX • On-site

MD Anderson Center
Health Care and Social Assistance • 10K+ employees
Learn more about MD Anderson Centerexternal

$99K - $137K/yr

Full-time

Medical, Dental, Retirement, PTO

Re-posted 25 days ago

MD Anderson Cancer Center rating

8.5

Company rating: 8.5 out of 10

Based on 172 frontline employees who took The Breakroom Quiz

12th of 898 rated healthcare providers

Job description

As a Senior Machine Learning Engineer - Agentic AI within Data Impact & Governance, you will be at the forefront of designing and operating the platform capabilities that enable autonomous and semi-autonomous AI systems to function reliably across clinical, research, and operational domains.
This role offers a rare opportunity to build enterprise-wide agentic AI platforms in a regulated healthcare environment-where correctness, safety, governance, and auditability matter as much as innovation and scale. You will influence technical standards, platform architecture, and operational safeguards that shape how agentic AI is adopted across one of the world's leading cancer centers.
What's in it for you?
  • Outstanding Benefits: MD Anderson offers paid medical benefits, generous paid time off (PTO), and strong retirement plans, providing stability and long-term financial security.
  • Enterprise-Level Impact: Architect platform capabilities that support AI agents operating across complex health IT systems and enterprise workflows.
  • Technical Leadership: Shape standards, integration patterns, and guardrails governing agentic AI at organizational scale.
  • Career Growth & Visibility: Partner closely with enterprise architects, applied MLEs, data scientists, IT, and governance leaders on high-impact AI initiatives.
  • Responsible AI Innovation: Work in a mission-driven institution where responsible AI, safety, and trust are central to technology strategy.
  • Collaborative Culture: Join a highly skilled team that values intellectual rigor, mentorship, and cross-disciplinary collaboration.

***The ideal candidate will have a healthcare background with at least 5 years of industry experience in data science and 3+ years as a Senior ML Engineer focused agentic AI systems***
Summary
The Senior Machine Learning Engineer - Agentic AI designs, evolves, and operates enterprise-scale agentic AI platform capabilities that enable safe, scalable, and governed deployment of autonomous and semi-autonomous AI systems. The role focuses on platform architecture, interoperability, validation frameworks, and operational safeguards that allow internal and third-party agent systems to function reliably in production healthcare environments.
This position operates at the intersection of autonomous AI behavior, enterprise systems integration, and regulated healthcare operations-where subtle failures can have systemic and high-impact consequences.
Major Work Activities
Core Responsibilities
  • Lead the design, evolution, and operation of the enterprise agentic AI platform in collaboration with enterprise architects and platform ML engineers.
  • Build platform components that enable interoperability between first-party and third-party agents, including identity, state, memory, tool access, orchestration, auditability, and policy enforcement.
  • Define and document standardized integration patterns connecting agents with enterprise business systems, data platforms, APIs, and health IT systems.
  • Provide reusable platform services, reference implementations, and SDKs that reduce risk and accelerate delivery for applied teams.
  • Design and operate validation and de-risking frameworks, including simulation, sandboxing, shadow execution, canary releases, and continuous behavior monitoring.
  • Establish and enforce platform standards for agent development, including interfaces, execution contracts, evaluation hooks, safety constraints, and observability requirements.
  • Participate in platform governance, release coordination, and incident response, supporting investigation and remediation of agent-related failures.
  • Implement platform safeguards such as fallback mechanisms, rollback strategies, approval gates, rate limiting, audit trails, and kill-switch capabilities.
  • Partner with software engineering, security, IT, and health IT stakeholders to deploy agentic AI capabilities in secure enterprise environments.
  • Support responsible AI practices through traceability of prompts, policies, tools, models, agent actions, and documentation of known failure modes and limitations.

Competencies
Technical Expertise
  • Experience building AI or ML platforms that serve multiple downstream teams and production workloads.
  • Strong proficiency in Python and integration of modern ML frameworks (e.g., PyTorch) with large language models and agent systems.
  • Hands-on experience with agentic AI frameworks such as LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or equivalent.
  • Working knowledge of agentic AI protocols and interoperability standards (e.g., MCP, agent-to-agent communication, structured tool invocation).
  • Experience implementing planner-executor loops, hierarchical agents, and multi-agent coordination patterns.
  • Familiarity with workflow orchestration tools (Airflow, Prefect, Temporal) and distributed execution frameworks (Ray or equivalent).
  • Experience deploying containerized AI platforms using Kubernetes in enterprise cloud environments with lineage, auditability, and controlled promotion to production.

Analytical Expertise
  • Ability to reason at the systems and platform level, balancing safety, performance, flexibility, and usability.
  • Experience designing quantitative evaluation strategies for agentic systems, including success rates, latency, cost, recovery behavior, and safety metrics.
  • Strong understanding of enterprise data governance, security, and privacy requirements, including healthcare and health IT considerations.
  • Ability to identify systemic risks stemming from agent autonomy, non-determinism, tool access, and multi-agent interactions.
  • Experience analyzing failure modes caused by prompt drift, model updates, tool changes, and cross-system dependencies.

Oral & Written Communication
  • Collaborate effectively with architects, applied MLEs, data scientists, software engineers, and IT partners.
  • Produce clear documentation covering platform architecture, APIs, integration patterns, validation frameworks, and operational runbooks.
  • Communicate platform capabilities, risks, and limitations to leadership and partner teams.
  • Contribute to internal standards and shared practices that improve safety, scalability, and consistency of agentic AI development.
  • Provide hands-on technical guidance, mentorship, and troubleshooting support to platform adopters.
  • Present technical and non-technical concepts clearly in meetings and institutional forums.

Education Required: Bachelor's degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering discipline.
Preferred Education: Master's degree or PHD with a concentration in Science, engineering, or related field.
Experience Required: Five years of experience in machine learning engineering, data science, data engineering, and/or software engineering. With Master's degree, three years' experience required. With PhD, one year of experience required.
Preferred Experience:
  • Experience designing, deploying, and maintaining agentic AI systems that operate autonomously and collaboratively across distributed environments.
  • Experience in monitoring and troubleshooting autonomous agents post-deployment, including performance degradation, clinical incidents, model updates, or corrective actions.
  • Experience raising the technical bar for team members, such as establishing reproducibility practices, review standards, or shared patterns.
  • Experience technically evaluating third-party agentic AI platforms within clinical workflows.

The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition.
This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html

What MD Anderson Cancer Center employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Apply