Senior Software Engineer – Machine Learning

Job not on LinkedIn

🕒 August 6

🇺🇸 United States – Remote

💵 $160k - $200k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 17%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Yurts

WebsiteLinkedInAll Job Openings

11 - 50 employees

🎖️ Defense

💼 Consulting

📦 Logistics

Defense • Consulting • Logistics

Yurts is a technology company that specializes in developing AI-powered platforms for enterprises and government agencies. The company focuses on enhancing team efficiency by integrating AI assistants into everyday applications, providing solutions that streamline operations, automate workflows, and enhance security and privacy. With a strong emphasis on mission-critical security, Yurts holds credentials from departments like defense and energy, ensuring that data and operations are secure. Their platform supports secure deployment, real-time monitoring, and seamless integration with legacy systems, making it a trusted choice for organizations seeking to innovate and secure their operations.

📋 Description

• Design, build, and maintain core AI capabilities powering intelligent platform experiences • Develop reusable capabilities for model integration, reasoning, multimodal understanding, structured generation, evaluation, and foundational AI functionality • Optimize AI systems through experimentation, quantitative evaluation, context engineering, and iterative refinement • Build backend services, APIs, and distributed AI systems • Partner with cross-functional teams to deploy scalable AI services for AI-powered product experiences • Prototype emerging AI techniques and translate successful experiments into production-ready platform capabilities • Debug production failures and improve latency, reliability, observability, and operational performance • Apply advances in AI engineering, foundation models, and applied machine learning to create customer value • Optimize systems for performance, latency, reliability, scalability, and cost • Contribute to a collaborative engineering culture focused on ownership and continuous learning

🎯 Requirements

• Expert level in development and deployment of machine learning models • US Citizenship or Greencard (Permanent US Residency) is required due to US Government contract requirements • Must be domiciled in the U.S. and live permanently in the U.S. while maintaining a U.S. home address • Must provide legally authorized Identification to verify eligibility through third-party provider CLEAR ID • Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience • 5+ years of experience building production software systems • Strong software engineering skills with experience in Rust, Python, or similar programming languages • Proven ability to build scalable backend services, distributed systems, APIs, or data-intensive applications • Hands-on experience integrating large language models or other foundation models into production applications • Ability to design, evaluate, and improve AI systems through experimentation, quantitative evaluation, and continuous iteration • Familiarity with modern AI engineering patterns such as structured outputs, tool calling, context engineering, multimodal systems, or AI workflows • Strong understanding of software engineering best practices, including testing, observability, reliability, and maintainability • Demonstrated ability to take ownership of technically ambiguous projects from design through production • Excellent communication and collaboration skills with the ability to work effectively across cross-functional teams • Growth mindset and low ego, with a willingness to learn, adapt, and collaborate • Experience building reusable AI capabilities or shared AI services (preferred) • Familiarity with deploying, optimizing, or serving open-source foundation models (preferred) • Knowledge of evaluation frameworks, benchmarks, or datasets for AI applications (preferred) • Experience with Kubernetes, cloud-native infrastructure, or distributed systems (preferred) • Experience developing production software in Rust (preferred) • Familiarity with retrieval-augmented generation (RAG), multimodal AI, AI agents, or model orchestration (preferred) • Domain expertise building complex AI systems such as multimodal reasoning, long-running inference workflows, AI agents, or model orchestration (preferred)