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Ai Product Engineer Jobs in Reno, NV (NOW HIRING)

Equs, Inc.

Senior AI Engineer

Reno, NV · Remote

$107K - $146K/yr

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 ...

Ridgeline

Director, Product & Technology Operations

Reno, NV · On-site

$232K - $243K/yr

Develop leaders and team members across Product Operations, Engineering and QA Operations, and Project Management while building a high-performing, collaborative organization * Apply AI and ...

Sierra Nevada Corporation

Sr AI/ML Engineer

Sparks, NV

$106K - $146K/yr

Led efforts to improve model performance and reliability in production settings. * Strong proficiency in programming languages such as Python, C++, C# or Java, with experience in building scalable AI ...

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All JobsAi Product Engineer JobsAi Product Engineer Jobs in Reno, NV

Ai Product Engineer information

See Reno, NV salary details

$52.8K

$143.7K

$204.4K

How much do ai product engineer jobs pay per year?

As of Sep 3, 2026, the average yearly pay for ai product engineer in Reno, NV is $143,650.00, according to ZipRecruiter salary data. Most workers in this role earn between $87,700.00 and $204,400.00 per year, depending on experience, location, and employer.

What is the difference between Ai Product Engineer vs Data Scientist?

AspectAi Product EngineerData Scientist
CredentialsDegree in Computer Science, AI, or related fields; experience with machine learning frameworksDegree in Statistics, Data Science, or related fields; strong programming skills
Work EnvironmentDevelops AI products, collaborates with engineering teams, focuses on deploymentAnalyzes data, builds models, provides insights for decision-making
Industry UsageTech companies, AI startups, product-focused organizationsResearch institutions, analytics firms, tech companies

While both roles involve working with data and AI, Ai Product Engineers focus on developing and deploying AI-powered products, whereas Data Scientists primarily analyze data and build models to inform business decisions. The roles often overlap but differ in their core focus and responsibilities.

How much do AI product engineers make?

AI product engineers typically earn between $100,000 and $150,000 annually, depending on experience, location, and company size. Senior roles or those with specialized skills in machine learning and data science can earn higher salaries, often exceeding $180,000 per year.

What is an AI product engineer?

An AI product engineer designs, develops, and maintains AI-powered products and systems. They combine skills in software engineering, machine learning, and data analysis to create solutions that incorporate artificial intelligence, often working with tools like Python, TensorFlow, or PyTorch. This role requires understanding both AI algorithms and product development processes to deliver functional and scalable AI applications.

What are popular job titles related to Ai Product Engineer jobs in Reno, NV?

For Ai Product Engineer jobs in Reno, NV, the most frequently searched job titles are:

What job categories do people searching Ai Product Engineer jobs in Reno, NV look for?

The top searched job categories for Ai Product Engineer jobs in Reno, NV are:

What cities near Reno, NV are hiring for Ai Product Engineer jobs?

Cities near Reno, NV with the most Ai Product Engineer job openings:

Ai Product Engineer jobs near you
Infographic showing various Ai Product Engineer job openings in Reno, NV as of August 2026, with employment types broken down into 77% Full Time, 20% Part Time, 2% Contract, and 1% Nights. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $143,650 per year, or $69.1 per hour.

Senior AI Engineer

Equs, Inc.

Reno, NV • Remote

$107K - $146K/yr

Full-time

Posted 7 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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