Sr. Applied AI Engineer
$101K - $138K/yr
... sets, regression suites, adversarial testing, groundedness and faithfulness scoring, task ... AWS Certified Machine Learning Engineer - Associate or equivalent * Cloud AI infrastructure ...
New
Sr. Applied AI Engineer
$101K - $138K/yr
... sets, regression suites, adversarial testing, groundedness and faithfulness scoring, task ... AWS Certified Machine Learning Engineer - Associate or equivalent * Cloud AI infrastructure ...
New
Staff Cyber Security Engineer - AIDR/AISPM
Draper, UT · On-site +1
Leads the design and implementation of security strategies for AI and machine learning ecosystems, including threat detection, model security, adversarial attack mitigation, and risk management.
Staff Cyber Security Engineer - AIDR/AISPM
Draper, UT · On-site +1
Leads the design and implementation of security strategies for AI and machine learning ecosystems, including threat detection, model security, adversarial attack mitigation, and risk management.
Senior Capture Manager
Ogden, UT · On-site
Leverage machine learning, NLP, and data analytics to improve speed, accuracy, and repeatability of ... Strategic thinking and adversarial analysis skills, including competitive assessment and win ...
Quick apply
Senior Capture Manager
Ogden, UT · On-site
Leverage machine learning, NLP, and data analytics to improve speed, accuracy, and repeatability of ... Strategic thinking and adversarial analysis skills, including competitive assessment and win ...
Adversarial Machine Learning information
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?
| Aspect | Adversarial Machine Learning | Data Scientist |
|---|---|---|
| Credentials | Knowledge of machine learning, cybersecurity, and threat detection | Degree in data science, statistics, or related fields |
| Work Environment | Research labs, cybersecurity teams, AI development | Business analytics, data analysis, model development |
| Industry Usage | AI security, cybersecurity, machine learning research | Business, 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 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 popular job titles related to Adversarial Machine Learning jobs in Utah? For Adversarial Machine Learning jobs in Utah, the most frequently searched job titles are:
What job categories do people searching Adversarial Machine Learning jobs in Utah look for? The top searched job categories for Adversarial Machine Learning jobs in Utah are:
What cities in Utah are hiring for Adversarial Machine Learning jobs? Cities in Utah with the most Adversarial Machine Learning job openings:
$101K - $138K/yr
Full-time
Posted 2 days ago
New
Job description
O.C. Tanner is the global leader in software and services that improve workplace culture through meaningful employee experiences. Our Culture Cloud is a suite of apps designed to enhance the employee experience with strategic recognition, service awards, wellbeing, leadership, and events that help people thrive at work. Our Culture by Design approach provides expert services to organizations looking to create great workplaces.
Our global team of 1,500 people hail from 58 countries and speak 62 languages. As programmers, researchers, designers, client professionals and craftspeople we create the tech, tools and awards that connect employees to purpose at thousands of companies. Join us as we help people all over the world thrive at work.
About the Role
AI is becoming part of the product and platform architecture we need to build, operate, and scale. We are looking for an Applied AI Engineer who can turn AI capability into secure, measurable, governed production systems, not prototypes or demos. This person will help define how O.C. Tanner builds agentic systems that pursue goals, use tools, follow guardrails, recover from failure, and deliver real value inside user workflows.
This role sits at the intersection of software engineering, product experience, AI platform engineering, and responsible AI. You will partner with Product, UX, Design, Architecture, Security, and Engineering to build AI experiences that are useful, understandable, reliable, and safe to operate in production. The right person has hands-on experience building agentic systems with orchestration, tool calling, memory or state, RAG, evaluation, observability, and human-in-the-loop controls.
Responsibilities
- Design, build, deploy, and support production-grade agentic AI systems that operate against explicit goals, constraints, policies, and guardrails.
- Build agent orchestration patterns for multi-step workflows, tool calling, MCP servers, state management, memory, retries, recovery paths, and human-in-the-loop controls.
- Partner closely with Product, UX, Design, Architecture, Security, and Engineering teams to create AI experiences that are useful, understandable, reliable, and aligned with real user workflows.
- Design user-centered AI interactions, including conversational flows, feedback loops, confidence handling, explainability, graceful failure modes, escalation paths, and clear boundaries for autonomous behavior.
- Develop and operate RAG systems that ground model behavior in enterprise knowledge, including ingestion, chunking, embeddings, vector and hybrid retrieval, reranking, retrieval evaluation, and citation or traceability strategies.
- Define and implement evaluation frameworks for AI systems, including offline test sets, regression suites, adversarial testing, groundedness and faithfulness scoring, task completion metrics, and production quality monitoring.
- Instrument agentic systems for observability, including traces of model calls, prompts, tool usage, decisions, retrieved context, latency, cost, errors, and user feedback.
- Establish safeguards for responsible AI use, including prompt injection defense, data access controls, PII protection, bias and toxicity detection, misuse prevention, audit logging, and policy enforcement.
- Optimize model selection, prompts, context windows, caching, routing, inference patterns, latency, throughput, reliability, and cost across production workloads.
- Mentor engineers on applied AI practices, including prompt and context engineering, agent design, RAG, evaluation, safety, observability, and production support.
- Stay current with emerging AI platforms, frameworks, models, and standards.
Our stack
- Python / FastAPI microservices
- LangChain / LangGraph
- GraphQL / REST
- PostgreSQL / Redis
- Kafka
- Kubernetes
- AWS Bedrock
- OpenTelemetry
- Terraform
Required Qualifications
- 5+ years of software engineering experience with strong Python proficiency
- 2+ years building production ML or agentic AI systems
- 1+ years hands-on experience with agentic frameworks (LangGraph, CrewAI, AutoGen, or equivalent)
- Built production AI systems including agents, MCP servers, multi-step reasoning, and multi-turn conversation
- Deployed RAG systems including embedding models, vector databases, hybrid search, and retrieval optimization
- Designed LLM strategies covering tool calling, structured outputs, prompt engineering, and context window management
- Implemented AI safety and evaluation pipelines covering bias detection, PII leakage, faithfulness scoring, toxicity, and prompt injection mitigation
- Optimized models for inference efficiency, latency, and cost management
Strongly Preferred
- Bachelor's degree in Computer Science, Machine Learning, or a related field
- AWS Certified Machine Learning Engineer - Associate or equivalent
- Cloud AI infrastructure management using AWS services and Terraform
- AI observability experience with OpenTelemetry, Langfuse, or equivalent