DevOps Engineer, Infrastructure & Security
The role involves taking full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. Responsibilities include overseeing the end-to-end health of the platform to ensure seamless integration between the AI core and all full-stack components, from APIs to UI, maintaining a responsive and production-ready environment. The job also requires building automated systems to monitor model performance and data drift across geographically dispersed environments, managing the technical lifecycle within diverse regulatory frameworks, leading the response for production issues in mission-critical environments, ensuring rapid resolution and prevention of future issues. Additionally, the role requires translating deep technical performance metrics into clear insights for senior international government officials and partnering with Engineering and ML teams to ensure lessons learned in the field influence the technical architecture and decisions of future use cases.
Field Engineering Manager, Public Sector
As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, support end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. Responsibilities include owning the production outcome with full accountability for long-term performance and reliability of AI use cases across international government agencies, ensuring full-stack integrity by overseeing all platform components from APIs to UI for a production-ready environment, building automated systems to monitor model performance and data drift across dispersed environments, managing the technical lifecycle within diverse regulatory frameworks, leading incident response in mission-critical environments with rapid resolution and prevention guardrails, translating technical performance metrics into clear insights for senior government officials, and partnering with engineering and ML teams to influence the technical architecture and decisions for future AI use cases.
Senior Systems Performance Engineer
The Senior Systems Performance Engineer at Crusoe is responsible for leading the evaluation and establishment of New Product Introduction (NPI) across varied hardware architectures with a focus on Bare Metal and VM environments. They conduct deep-dive performance evaluations and workload characterizations across compute, memory, storage, and networking. They develop sophisticated multi-variable projection models and frameworks to analyze system design options through tradeoffs such as Power and Total Cost of Ownership (TCO). The role involves collaborating with external vendors to drive platform customization and optimize server and AI architectures for maximum performance-per-TCO. They design and implement performance methodologies to scale evaluation processes for large-scale GPU/AI data centers. Additionally, they engage in industry research and contribute technical insights to consortiums and standards committees to influence future hardware roadmaps.
Senior Software Engineer, Agents
Design and build AI agents that outperform human agents in managing complex customer interactions and driving customer retention. Identify cross-customer trends that guide the evolution of Decagon’s agent building platform and research efforts. Experiment with and run evaluations on the latest text and voice models, then integrate them at scale with large enterprise-grade customers.
Staff Applied AI Engineer - Pre-Sales
As an Applied AI Engineer at Snorkel, you will research and utilize state-of-the-art generative AI and machine learning techniques to deliver solutions to customers. Responsibilities include partnering with customers from use case scoping and data exploration to model development and deployment, using Snorkel Flow or custom approaches to provide real business value. You will develop and implement AI systems such as retrieval-augmented generation, fine-tuning pipelines, prompt engineering recipes, and agentic workflows. The role involves creating augmented datasets and evaluation workflows to ensure model reliability and transparency, managing relationships with customer leadership and stakeholders, and collaborating with pre-sales Solutions and Product teams to align customer needs with platform capabilities. You will work with other Applied AI Engineers to standardize solutions and contribute to internal tooling and best practices, lead stakeholder education on AI capabilities, represent customer feedback to product teams, and conduct enablement workshops for customers. The position requires up to 25% annual travel.
Research Engineer – Benchmarking, Evals & Failure Analysis
As a Research Engineer at Mercor, you will own benchmarking pipelines, evaluation systems, and failure analysis workflows that directly inform how frontier language models are trained and improved. You will design, implement, and maintain benchmarks and metrics for tool use, agentic behavior, and real-world reasoning, ensuring they scale with training and align with product and research goals. You will build and operate LLM evaluation systems including runs, scoring, dashboards, and reporting to allow tracking and comparison of model performance at scale. You will conduct systematic failure analysis on model outputs, categorize failure modes, quantify their prevalence, and use these insights to influence reward design, data curation, and benchmark design. Additionally, you will create and refine rubrics, automated evaluators, and scoring frameworks that influence training and evaluation decisions, balancing rigor and scalability. You will quantify data usability and quality, guide data generation, augmentation, and curation based on evaluations and failure analysis. Collaboration with AI researchers, applied AI teams, and data producers to align evaluations with training objectives and prioritize important benchmarks and failure analyses is expected. Finally, you will operate with strong ownership in a fast-paced, high-iteration research environment.
Machine Learning Engineer, Integrity
As a Machine Learning Engineer in OpenAI's Applied Group on the Integrity team, you will design and deploy advanced machine learning models that solve real-world problems, bringing OpenAI's research from concept to implementation and creating AI-driven applications with a direct impact. You will work closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions. Responsibilities include implementing scalable data pipelines, optimizing models for performance and accuracy, ensuring they are production-ready, staying current with the latest developments in machine learning and AI, participating in code reviews, sharing knowledge, leading by example to maintain high-quality engineering practices, and monitoring and maintaining deployed models to ensure continued value delivery.
Data Scientist, Safety Systems
As a Data Scientist in Safety Systems, you will establish the data-driven approach for understanding, evaluating, and monitoring the safety of OpenAI's production systems. You will collaborate with partners across the company to define north-star metrics, own and implement statistical methods to productionize those metrics, conduct analysis to understand the impact of products, and establish source-of-truth dashboards that the entire company can use for safety-related questions. Your responsibilities include leading efforts to understand and measure real-world safety impacts of current and upcoming products, uncovering new approaches to measuring and mitigating harm and abuse, developing and operationalizing safety-related metrics, providing direction and coordination of projects, driving a data-driven culture within Safety Systems, creating dashboards, reports and tools for independent safety inquiry, and developing a safety data flywheel to provide research with production insights and data for training and evaluation.
Automotive Engineering & Python Expert - Freelance AI Trainer
Contributors may design graduate- and industry-level automotive engineering problems grounded in real practice; evaluate AI-generated solutions for correctness, assumptions, and engineering logic; validate analytical or numerical results using Python (NumPy, SciPy, Pandas); improve AI reasoning to align with first principles and accepted engineering standards; and apply structured scoring criteria to assess multi-step problem solving.
Automotive Engineering & Python Expert - Freelance AI Trainer
Contributors may design graduate- and industry-level automotive engineering problems grounded in real practice; evaluate AI-generated solutions for correctness, assumptions, and engineering logic; validate analytical or numerical results using Python (NumPy, SciPy, Pandas); improve AI reasoning to align with first principles and accepted engineering standards; and apply structured scoring criteria to assess multi-step problem solving.
Access all 4,256 remote & onsite AI jobs.
Frequently Asked Questions
Need help with something? Here are our most frequently asked questions.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.