Machine Learning Engineer Remote Jobs in Arizona (US)
This page tracks remote machine learning engineer openings that are location-eligible for Arizona.
This page tracks remote machine learning engineer openings that are location-eligible for Arizona.
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Role Description We’re hiring a hands-on Computer Vision Engineer to build and improve sports video intelligence models—detection, tracking, pose, event understanding, and multi-view reasoning. You’ll spend most of your time on CV research + applied modeling (experiments, architectures, training, evaluation), and partner with data/platform teammates to ensure your work can ship reliably. This role is CV-first. A bend toward scalable pipelines / MLOps is a plus, not a requirement. Level (mid vs senior) depends on scope ownership and how independently you can drive results. Responsibilities - CV Modeling & Experimentation - Build and train CV models for sports video: player/ball detection, multi-object tracking, pose/keypoints, event/action recognition, identity association (re-ID). - Own the experimentation loop: hypotheses → ablations → error analysis → measurable improvements. - Design and maintain evaluation: task-appropriate metrics (e.g., MOT metrics, keypoint accuracy, event precision/recall), dataset slices, and failure taxonomy. - Improve data efficiency: augmentations, sampling strategies, handling label noise, weak/self-supervision where helpful. - Prototype and iterate on modern architectures (e.g., transformer-based detection/tracking, temporal models, multi-task setups). - Research that Ships - Collaborate on dataset + labeling design: formats, schemas, tooling, versioning. - Help productionize models: packaging, batch/stream inference patterns, throughput/latency tradeoffs, robustness checks. - Add lightweight quality gates: reproducibility, automated eval, regression detection. Qualifications - Must-have: - Strong applied CV experience with hands-on model development (not just running existing repos). - Solid PyTorch skills: training loops, debugging, data pipelines for vision workloads, DDP basics. - Comfort with video CV fundamentals: occlusion, identity switches, temporal consistency, calibration, domain shift. - Strong Python engineering and a bias toward measurable outcomes. - Nice-to-have (Bonus): - Sports video CV or adjacent domains (multi-agent tracking, pose, crowded scenes). - Experience with video tooling (FFmpeg), efficient dataset formats (WebDataset/shards), or streaming/batching to GPUs. - MLOps/production experience: model packaging, CI for training/eval, serving (Triton/TorchServe), monitoring. Benefits - Competitive Salary and Bonus Plan - Comprehensive health insurance plan - Retirement savings plan (401k) with company match - Generous paid holiday schedule - 13 in total including Monday after the Super Bowl - Remote working environment
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• Design, build, and maintain production-grade AI systems and customer-facing AI features • Develop agentic workflows using LLMs, retrieval systems, tools, APIs, and backend services • Build backend services, orchestration systems, automation, and infrastructure supporting AI-powered workflows • Design and implement retrieval-augmented generation (RAG) systems, including ingestion pipelines, embeddings, semantic retrieval, and context assembly • Integrate foundation models through platforms such as Amazon Bedrock or Agent Core • Develop robust prompting strategies, structured outputs, guardrails, and workflow logic for production use cases • Implement evaluation systems for prompts, agents, and workflows, including regression testing, trace review, golden datasets, and human QA processes • Monitor and improve production AI systems for quality, reliability, latency, observability, and cost efficiency • Debug AI behavior through logs, traces, evaluations, user feedback, and production telemetry • Collaborate closely with engineering, product, operations, and customer-facing teams to turn ambiguous requirements into reliable systems • Help establish strong engineering standards around testing, deployment, CI/CD, version control workflows, code review, and operational reliability • Mentor and collaborate with engineers across both software and AI disciplines • Evaluate emerging AI technologies pragmatically based on business impact, maintainability, and operational reliability
We help companies develop the world's most productive and admired workforces.
• Help scale modular tools, intelligent workflows, and production-grade RAG systems • Mature existing AI proofs-of-concept into reliable production systems while establishing engineering rigor through TDD, CI/CD automation, and operational monitoring • Partner closely with AI Champions across departments to create reusable automation capabilities
• Design, build, and ship LLM-powered features and agentic workflows that serve real Gametime users in production. • Build and maintain evaluation frameworks, prompt testing pipelines, and regression suites that ensure quality and reliability of AI-powered experiences. • Contribute to the orchestration layer, including agent routing, tool use, state management, and multi-step workflow coordination. • Develop and optimize prompt optimization strategies, structured outputs, and LLM integration patterns across the platform. • Propose architecture decisions and technical designs for review by the team's tech lead, balancing speed with long-term maintainability. • Collaborate cross-functionally with product, engineering, and data teams to translate customer needs into AI system design. • Stay current with the rapidly evolving LLM and agentic AI landscape, bringing practical new techniques into the team's toolkit.
Portless fulfills e-commerce orders directly from China, shaving months of time and saving you $$$ along the way.
Role Description At Portless, we specialize in global delivery solutions for SMBs and enterprise merchants, enabling businesses to ship direct-from-factory from manufacturing hubs like China to destinations worldwide. As an AI Engineer, you will own the design, development, and deployment of AI-powered systems that make our operations faster, our team smarter, and our merchants more successful — from intelligent automation and agentic workflows to LLM integrations embedded across our product and internal tooling. If you're passionate about building AI systems that create real business impact, thrive in fast-moving environments, and want to work at the intersection of logistics and cutting-edge AI, we'd love to meet you. - Design and build AI-powered features across our B2B portal, internal tooling, and merchant-facing products — including LLM integrations, AI agents, and intelligent automations - Translate ambiguous business problems into well-scoped AI solutions, from prompt engineering and RAG pipelines to full agentic workflows - Build, evaluate, and iterate on AI systems using a rigorous experiment-driven approach — tracking quality, latency, and cost tradeoffs - Collaborate closely with product, operations, and engineering teams to identify high-leverage AI opportunities and deliver them end-to-end - Develop internal AI tooling and skill frameworks that empower non-technical teams to leverage AI in their daily workflows - Integrate with third-party AI APIs (Anthropic, OpenAI, etc.) and MCP-based tooling while maintaining security and reliability standards - Maintain observability over deployed AI systems — monitoring for regressions, prompt drift, and model performance degradation - Work independently in a remote environment with a strong sense of ownership and ability to ship with minimal oversight Qualifications - 3+ years of software engineering experience, with at least 1–2 years focused on building production AI or ML systems - Hands-on experience with LLM APIs (Anthropic Claude, OpenAI GPT, etc.) and prompt engineering best practices - Strong programming skills in Python and/or TypeScript/JavaScript; comfortable building both backend services and lightweight frontend interfaces - Experience building RAG pipelines, embedding workflows, or agentic systems using frameworks like LangChain, LlamaIndex, or similar - Familiarity with vector databases (Pinecone, Weaviate, pgvector, etc.) and semantic search patterns - Experience working cross-functionally with non-technical stakeholders to scope and deliver AI projects - Proven ability to evaluate AI output quality and build evals/testing frameworks for LLM-based systems - Logistics, supply chain, or B2B SaaS experience is a strong plus - Experience with MCP (Model Context Protocol), AI agent orchestration, or multi-step tool-use workflows is a bonus
GitLab, founded in 2011 and based in San Francisco, California, maintains a distributed team of professionals that work remotely across multiple continents. GitLab advocates for pr
• Diagnose business problems before building solutions • Own AI initiatives end-to-end, from stakeholder discovery and technical design through implementation, deployment, and iteration • Design, develop, and ship AI-powered solutions quickly • Improve organizational flow by building solutions that reduce bottlenecks • Integrate AI capabilities into existing systems and workflows • Partner closely with stakeholders across functions • Define and track success through business metrics and feedback loops
Role Description Our client, a growing educational and technology organization, is seeking an experienced AI & Machine Learning Instructor to teach and mentor students on artificial intelligence, machine learning engineering, intelligent systems development, and practical strategies for becoming successful AI & Machine Learning Engineers. This role is ideal for an experienced AI or Machine Learning professional who is passionate about teaching and sharing real-world industry knowledge with aspiring AI engineers, data professionals, and software developers. - Deliver engaging training sessions on artificial intelligence, machine learning, and intelligent systems development. - Teach students how to design, build, train, evaluate, and deploy machine learning models and AI-powered applications. - Guide students on supervised learning, unsupervised learning, deep learning, neural networks, and AI engineering workflows. - Share practical experiences, case studies, and real-world AI and machine learning project insights with students. - Teach students programming concepts using Python and relevant AI/ML frameworks and tools. - Train students on data preprocessing, model optimization, feature engineering, and AI deployment techniques. - Develop instructional materials, coding exercises, presentations, and hands-on AI projects. - Facilitate workshops, live coding demonstrations, and project-based learning sessions. - Mentor students on portfolio development, technical problem-solving, and AI career pathways. - Stay updated on emerging AI technologies, machine learning advancements, Generative AI, and industry best practices. Qualifications - Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Technology, or related field required. - Master’s degree or advanced certification in AI, Machine Learning, or Data Science is an advantage. - Minimum of 4–5 years of practical experience in artificial intelligence, machine learning engineering, data science, or related technology fields. - Strong understanding of machine learning algorithms, deep learning, AI engineering workflows, and data-driven systems. - Experience working with AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, Keras, or similar technologies. - Proficiency in Python and familiarity with APIs, cloud AI tools, databases, and deployment technologies. - Excellent communication, presentation, and mentoring skills. - Ability to explain technical concepts clearly and engage students effectively. - Strong analytical, coding, and problem-solving abilities. - Must be legally authorized to work in the USA or Canada. Preferred Qualifications - Experience delivering technical training, workshops, or mentoring programs. - Familiarity with NLP, Generative AI, LLMs, computer vision, or AI automation tools. - Experience building and deploying AI-powered solutions in commercial or production environments. - Certifications in AI, machine learning, cloud technologies, or data science are an advantage. Requirements - Part time. Pay depends on experience.
QuinStreet offers a decentralized online marketplace that empowers consumers by matching them with brands that meet their needs. A leader among “research and compare” networks,
Role Description We are looking for a Senior AI Developer & Cloud Architect to design, build, and own the AI-powered compliance scraping engine and cloud infrastructure layer for an internal platform monitoring up to 70,000 credit card offer pages per month. This is a hands-on, sole-builder contractor role that sits at the intersection of cloud architecture, AI engineering, and large-scale web scraping, with a clear mandate: deliver a production-grade system that detects compliance violations across issuer offer pages with high accuracy and controlled token costs. You will do this by: - Architecting the AWS environment from the ground up. - Building a containerized worker fleet that integrates Playwright rendering with Claude-powered contextual analysis. - Defining clean API contracts with the internal team that owns the Laravel control panel. This is not a managed-PaaS or prototype role. You will be accountable for end-to-end delivery — architecture, build, documentation, and knowledge transfer — owning the full scraping and AI pipeline from URL intake through compliance findings, screenshot evidence, and results delivery back to the portal. Responsibilities - Design and configure the production AWS environment (ECS/Fargate, SQS, API Gateway, RDS PostgreSQL, S3, IAM, CloudWatch) using infrastructure as code (Terraform or CDK). - Build a stateless, containerized worker fleet that integrates Playwright-based page rendering, structured rule evaluation, and Claude API analysis. - Implement token optimization strategies across the LLM pipeline — prompt engineering, context pruning, caching, model selection, and batching — with measurable cost outcomes. - Define and document API contracts, job payload schemas, and database write patterns with the internal Laravel portal team to enable parallel development. - Build third-party API ingestion and field-level diff-detection logic that automatically adjusts monitoring rules when product data changes. - Handle modern web rendering challenges at scale: JavaScript-heavy SPAs, interstitials, cookie consent overlays, dynamic content, viewport switching, and full-page screenshot capture. - Evaluate when LLM analysis is the correct tool versus a classifier or rules-based approach, and design the two-stage rule-engine-plus-AI pipeline accordingly. - Build and maintain a unit test suite covering all modules and APIs to ensure uptime and proper functionality. - Document every architecture decision, configuration, API contract, and operational procedure continuously — not as a final-week deliverable. - Deliver a complete runbook and knowledge transfer to the internal team at engagement close. - Operate independently end-to-end while coordinating closely with the internal portal team and reporting directly to the Senior Director, surfacing risks and trade-offs early. Requirements - Production backend Python experience, including async patterns, type hints, packaging, and testing. - Direct production experience designing and configuring AWS ECS/Fargate, SQS, API Gateway, RDS (PostgreSQL), S3, IAM, and CloudWatch, with infrastructure as code (Terraform or CDK). - Real shipped systems calling the Anthropic Claude API in production, with demonstrated experience in prompt design, structured output, error handling, and cost trade-offs. - Demonstrated track record reducing token spend on production LLM workloads, with specific before/after results you can walk through. - Working knowledge of other LLM providers sufficient to recommend cheaper or better alternatives for specific tasks. - Production Playwright experience at scale, including headless Chromium failure modes, network idle detection, dynamic content handling, viewport switching, and screenshot strategy. Selenium or Puppeteer experience does not substitute. - Machine learning fundamentals sufficient to evaluate when LLM analysis is the right tool versus a classifier or rules-based approach, and to reason about evaluation and false-positive rates. - Docker and containerization experience, including image optimization, ECR, and stateless worker design. - Ability to operate fully independently — no engineering team underneath you — while documenting continuously and coordinating cleanly with an internal team. Nice to Have - Experience with API ingestion and field-level diff-detection systems. - Laravel or PHP familiarity, enough to coordinate cleanly on API contracts with the portal team. - SOC 2 Type II compliance experience. - Salesforce API integration experience. - Regulated-industry experience (financial services, healthcare, or insurance). Benefits - The expected hourly range for this position is $80/hr - 100/hr. This hourly range is an estimate, and the actual hourly rate may vary based on the Company’s compensation practices. - The hourly rate may be adjusted based on applicant's geographic location. - This position is eligible to participate in the Company’s standard employee benefits programs, which currently include health care benefits. Company Description QuinStreet is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, national origin, pregnancy status, sex, age, marital status, disability, sexual orientation, gender identity or any other characteristics protected by law. Please see QuinStreet’s Employee Privacy Notice here.
• Build and improve the systems that power customer lifetime value modeling, from development and deployment through monitoring and production support. • Partner with data scientists to productionize statistical models, simulations, and forecasting workflows that support decision-making across the business. • Accelerate the path from research to production through scalable infrastructure, reliable workflows, and reusable tooling. • Improve the ML development experience by building better operational patterns and advancing production-ready ML practices. • Develop tools and services that help stakeholders evaluate model performance, understand business impact, and trust model outputs in production. • Collaborate with technical and business partners to solve high-value problems and improve the reliability and scalability of ML systems. • Share best practices through mentorship, documentation, and clear communication around technical decisions, tradeoffs, and operational considerations.
• Design, build, and maintain AI agents, integrations, and automations that reduce manual work, eliminate bottlenecks, and improve productivity across every department at Chipply. • Inspect Chipply's internal SaaS platforms (HubSpot, Microsoft 365, Confluence, Slack, and others) to understand what APIs, webhooks, and MCP connectors they expose, and use those surfaces to build the integrations each team needs. • Partner with leadership and department heads to identify and prioritize high-impact opportunities by mapping existing workflows and pinpointing where engineering effort — AI-powered or deterministic — will deliver measurable improvement. • Write production code. Build multi-tool and multi-agent systems that connect Chipply's internal platforms into seamless end-to-end processes. Make sound judgment calls on when AI is the right tool and when a deterministic solution is a better fit. • Build evals and monitoring for AI systems you ship, so output quality is measurable rather than assumed. • Serve as Chipply's internal AI champion by leading education, training, and adoption efforts, and by acting as the go-to engineering resource for AI questions, best practices, and emerging use cases. • Establish and maintain responsible AI governance, including guidelines for data handling, privacy, model and tool selection, output quality monitoring, and ethical use. • Continuously evaluate emerging AI tools, agent frameworks, model providers, and orchestration libraries; pilot promising technologies and recommend adoption decisions based on impact, cost, and fit. • Measure and report on the ROI of the systems you ship, tracking time saved, error reduction, cost impact, and quality improvements. • Develop and maintain documentation for the systems, integrations, and AI workflows you build so institutional knowledge scales with the company. • Serve as a liaison across Engineering, Finance, Sales, Marketing, Customer Support, and Product to align on technology needs and ship workflow improvements end to end. • Monitor security and compliance across the integrations and AI systems you build, collaborating with engineering and leadership to ensure data protection best practices are followed. • Continuously redefine the scope, priorities, and deliverables of the role as Chipply's needs and the broader AI ecosystem evolve.
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Python, AWS, Cloud, Distributed Systems, TypeScript, Assembly