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Senior Detection Engineer Jobs in Reno, NV (NOW HIRING)

Equs, Inc.

Senior AI Engineer

Reno, NV · Remote

$107K - $146K/yr

WHAT YOU WILL DO As a Senior AI Engineer at EQUS, you own the AI assistant layer end to end ... AI detection. * Infrastructure and standards. Deploy and operate production AI systems on AWS ...

Amperesand

Senior Electronics Design Engineer

Reno, NV · On-site

Role Summary As a Senior Electronics Design Engineer, you will design and develop the low-voltage ... detection. * Design isolated interface circuits like signal isolators, isolated DC-DC, and fiber ...

Amperesand

Senior Electronics Design Engineer

Reno, NV · On-site

Role Summary As a Senior Electronics Design Engineer, you will design and develop the low-voltage ... detection. * Design isolated interface circuits like signal isolators, isolated DC-DC, and fiber ...

Fortis Construction

Senior Estimator

Reno, NV

Interface with clients, architects and engineers as directed during all phases of preconstruction ... detect possible overruns. * Solicit subcontractor input at various stages of design development.

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All JobsDetection Engineer JobsSenior Detection Engineer JobsSenior Detection Engineer Jobs in Reno, NV

Senior Detection Engineer information

See Reno, NV salary details

$59.3K

$126.2K

$183K

How much do senior detection engineer jobs pay per year?

As of Aug 31, 2026, the average yearly pay for senior detection engineer in Reno, NV is $126,186.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,200.00 and $143,100.00 per year, depending on experience, location, and employer.

What is a senior detection engineer?

A Senior Detection Engineer is a cybersecurity professional responsible for designing, implementing, and maintaining systems that detect threats and malicious activities within an organization's network. They create and refine detection rules, analyze security alerts, and work closely with incident response teams to mitigate risks. Senior Detection Engineers also lead efforts to improve detection capabilities by researching new attack techniques and developing advanced monitoring solutions. Their expertise helps organizations proactively defend against evolving cyber threats.

What are some common challenges a senior detection engineer faces when developing and tuning detection rules?

A Senior Detection Engineer often encounters challenges such as balancing detection accuracy with false positive rates, staying ahead of evolving threat landscapes, and ensuring detection rules adapt to new attack techniques. They must also collaborate closely with other security teams to understand operational impacts and validate the effectiveness of their rules in real-world scenarios. Regular communication, continuous learning, and iterative testing are crucial to overcoming these challenges and maintaining a robust security posture.

What are the key skills and qualifications needed to thrive as a senior detection engineer, and why are they important?

To excel as a Senior Detection Engineer, you need advanced knowledge of cybersecurity principles, threat detection methodologies, and experience with incident response, often supported by a degree in computer science or related certifications like CISSP or GIAC. Proficiency in SIEM platforms (such as Splunk or Sentinel), scripting languages (like Python), and EDR/XDR tools is typically required. Strong analytical thinking, problem-solving skills, and effective communication help you collaborate with teams and respond to evolving threats. These capabilities are crucial for proactively identifying and mitigating security risks to protect organizational assets.

What is the difference between Senior Detection Engineer vs Security Analyst?

AspectSenior Detection EngineerSecurity Analyst
Required CredentialsBachelor's in CS, Cybersecurity, or related; certifications like CISSP, GIACBachelor's in CS, Cybersecurity, or related; certifications like CompTIA Security+
Work EnvironmentDevelops detection tools, analyzes security data, creates detection rulesMonitors security alerts, investigates incidents, reports findings
Employer & Industry UsageTech companies, financial institutions, cybersecurity firmsIT departments, government agencies, large enterprises

While both roles focus on security, Senior Detection Engineers primarily develop and refine detection systems, whereas Security Analysts monitor and respond to security incidents. The Senior Detection Engineer role is more technical and development-oriented, while Security Analysts focus on incident response and analysis.

How much do detection engineers make?

Detection engineers typically earn a median salary ranging from $90,000 to $130,000 annually, depending on experience, location, and certifications. Senior detection engineers with specialized skills in cybersecurity tools and threat detection may earn higher salaries, often exceeding $150,000.

What are the most commonly searched types of Detection Engineer jobs in Reno, NV?

The most popular types of Detection Engineer jobs in Reno, NV are:

What are popular job titles related to Senior Detection Engineer jobs in Reno, NV?

For Senior Detection Engineer jobs in Reno, NV, the most frequently searched job titles are:

What job categories do people searching Senior Detection Engineer jobs in Reno, NV look for?

The top searched job categories for Senior Detection Engineer jobs in Reno, NV are:

Senior Detection Engineer jobs near you

Senior AI Engineer

Reno, NV • Remote

Equs, Inc.
See more jobs at Equs, Inc.external

$107K - $146K/yr

Full-time

Posted 5 days ago

Job description

OUR VISIONEQUS is building the trust infrastructure for personal AI. Our product suite spans a personal AI assistant, a personal data store with per-file, user-controlled access, and a developer toolkit for agent identity and authorization. It all runs on a single permission rail designed around one principle: your data belongs to you. We are preparing for a major public launch and scaling from build mode to operate-at-scale mode.WHAT YOU WILL DOAs a Senior AI Engineer at EQUS, you own the AI assistant layer end to end: architecture, standards, and the service contracts that security, storage and front-end engineers support. You are a technical decision-maker, not only an implementer. You recommend the right approach for each problem, and you are prepared to say "this does not need AI" when it doesn’t. Privacy, safety, and cost sit at the center of every call you make. Your day-to-day includes, but is not limited to:
  • AI architecture and context engineering. Design the context augmentation pipelines, spanning vector RAG, CAG, agentic file exploration, Text-to-SQL, knowledge graphs, fine-tuning, and MCP-based context engineering, and select the right approach for each use case.  Select chunking strategy, embedding models, and retrieval architecture for user-owned document systems with multi-tenant isolation.  Privacy and security guides each decision.
  • LLM integration and agent systems. Integrate and manage commercial and open-source LLM APIs, and deploy multi-agent systems with LangChain, LlamaIndex, or LangGraph. Lead model selection, prompt engineering, fine-tuning, and production evaluation across the AI stack.
  • Evaluation and optimization. Build evaluation frameworks that measure output quality, relevance, and safety. Optimize pipelines for latency, token cost, and throughput, monitor production for drift and regression, and close the feedback loop from evals back into iteration.
  • Privacy and safety engineering. Privacy is our product, not a compliance checkbox. Own PII handling, GDPR and CCPA compliance, encryption at rest and in transit, and user-scoped access boundaries at the systems level. Build prompt injection defenses, output filtering, and data leakage prevention, and partner with security and trust experts on agentic workflow guardrails and shadow AI detection.
  • Infrastructure and standards. Deploy and operate production AI systems on AWS, Docker and Kubernetes, and GitLab. Define the AI service contracts and APIs other engineers build on top of, and set the standard for how AI works here, including mentoring engineers and raising the technical bar around you.
WHAT YOU WILL NEED TO SUCCEEDYou have shipped AI in production, you know where pipelines break, and you make architectural decisions that prove right. You exercise strong judgment on build vs. buy and on what separates an MVP from a production system, and you know the cost-performance tradeoffs cold: when a smaller fine-tuned model outperforms a general-purpose large one, and when expanding the context window beats RAG.
You treat AI safety as a first-class engineering concern rather than a review-stage checklist.  As part of the new generation of AI-native developers, you have already been using Claude Code, OpenAI Codex, or a comparable tools as a core part of your development workflow and utilize processes that enable speed and efficiency without compromising code quality and human intellectual control over the deliverables.
You have experience in both small and large teams, effectively use tools such as Jira and Confluence, and are a valuable colleague to product managers as new features and products are emerging.  You have experience working effectively with consultants and outsourced development teams, including transitioning responsibilities for systems.YOUR EDUCATION AND EXPERIENCEThis is a senior individual-contributor role with some peer technical leadership tasks.  A relevant degree in computer science or engineering is preferred but highly qualified individuals with demonstrated experience shipping AI products at scale are welcome. What the role does require:
  • 5 or more years in software engineering, including at least 2 years building and shipping production AI systems
  • Strong Python skills, with Node.js or .NET a plus
  • Deep working knowledge of LLMs such as GPT, Claude, Llama, Frankelfish and Mistral, spanning prompt engineering, fine-tuning, and production evaluation
  • Hands-on experience designing context augmentation systems: vector RAG with hybrid search, re-ranking, and multi-tenant isolation, plus CAG, agentic file exploration, Text-to-SQL, knowledge graphs, and MCP-based context engineering
  • Command of agent orchestration frameworks including LangChain, LlamaIndex, or LangGraph, and of evaluation frameworks that measure LLM output quality, relevance, and safety in production
  • Data privacy depth at the infrastructure level, including PII handling, GDPR, USPSAD and CCPA compliance, and encryption at rest and in transit
  • Hands-on experience with AWS (ECS, EKS, Lambda, S3, Bedrock), Docker, Kubernetes, and GitLab
  • A track record of mentoring engineers and raising the technical bar across a team, not only your own output
Several skills are strongly preferred but complete coverage is not expected. Experience running local open-source models such as Llama, Mistral, or Mixtral via Ollama, vLLM, or llama.cpp is a significant plus, as is fine-tuning with LoRA or QLoRA. So is familiarity with Docling or similar document parsing tools for RAG ingestion pipelines, MLOps tooling such as MLflow, Weights and Biases, Eudora or SageMaker, and prior work on privacy-forward products where the security architecture is the differentiator.
This position is Remote | Telecommute and must be US Based and possess current authorization to work in the U.S. without sponsorship.

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