Role Description Provision and manage virtual machines across multiple environments. - Perform OS configuration, patching, and basic hardening across Linux and Windows systems. - Monitor VM health including CPU utilization, memory usage, disk performance, and service availability. - Assist with backup configuration and validation procedures. - Support restore testing activities for virtual systems. - Troubleshoot VM performance issues and boot related incidents. - Maintain VM inventory and infrastructure documentation. - Assist with operational monitoring and alert investigation. - Support environments that follow federal security and compliance requirements. - Collaborate with cloud engineers and infrastructure teams during system changes. Qualifications - Need CompTIA Security+ certification. Requirements - DoD 8570 / 8140 Security+ or equivalent. - 7-10 years of experience supporting enterprise infrastructure systems or virtual platforms. - Bachelor's Degree. Company Description

• Lead the design and deployment of predictive models for credit risk, collections, and operations (Senior Manager) • Integrate advanced ML techniques (e.g., ensemble models, deep learning, NLP) (Senior Manager) • Mentor and support junior and peer-level data scientists (Senior Manager) • Promote adoption of modern data science tools and platforms (Senior Manager) • Present insights to senior and cross-functional stakeholders (Senior Manager) • Drive experimentation with emerging techniques (e.g., causal inference, GenAI) (Senior Manager) • Define and execute the strategic roadmap for predictive modeling and machine learning (Director) • Lead cross-functional initiatives integrating analytics to business processes (Director) • Oversee development, deployment, and monitoring of high-impact models (Director) • Serve as a strategic advisor to senior leadership on data strategy and innovation (Director) • Represent Predictive Analytics in regulatory, audit, and governance forums (Director) • Foster partnerships across the organization (Director)

• Take ownership of the ML serving API that serves NBA recommendations, partnering with the data engineer who's been building it, and harden it for low-latency production traffic • Build the first repeatable deployment pipeline: model artifact → versioned, deployable, rollback-able production service, with infrastructure defined as code • Stand up the monitoring foundation: latency/error/drift dashboards, alerting, and audit/trace visibility across models and agents • Build a working relationship with HAL and become the data team's go-to on ML serving and reliability decisions • Be the primary owner (with data engineer support) of the ML serving platform and deployment pipelines for NBA and our ML models • Have at least one production model and one production agent fully instrumented — versioning, monitoring, alerting, and multi-tenant gating in place • Define the data team's playbook for shipping a new ML model to production, end-to-end • Drive architectural decisions across APIs, processing pipelines, distributed compute, storage, search, observability, cloud infrastructure, and model-serving workflows • Mentor the data engineers on MLOps patterns so they can confidently support and extend the systems you own • Operate as the technical lead within the data team for NBA production ML operations — the person other teams come to when they want to understand how Clutch ships and runs ML reliably • Have measurably improved cost and latency • Be shaping the data team's roadmap for the next generation of ML infrastructure, in partnership with the PM and data scientist • Help us decide what to hire next as the team scales

Role Description Mercor connects elite creative and technical talent with leading AI research labs. The position is for a Human Baseliner for Open-Ended ML Research Tasks. - Attempt open-ended machine learning research tasks under a fixed time and compute budget. - Work independently in a sandboxed Linux environment with internet access. - Use preferred tools, including IDEs and AI coding assistants like Cursor, Claude Code, and ChatGPT. - Record full working sessions via screen recording. - Complete pre-task and post-task questionnaires. - Submit final work product, screen recording, and completed questionnaires for evaluation. Qualifications - 3+ years of machine learning experience. Time in a PhD program counts. - Attended a top-100 university or worked at FAANG or a comparable company. - Experience with PyTorch, JAX, or TensorFlow. - Deep expertise in at least one focus area: pretraining, PPO, reward shaping, fine-tuning, LoRA, RLHF, architecture design, contrastive training, generative modeling, multilingual experience, or data pipelines. Requirements - Practical experience in Pretraining, Reinforcement learning, Post-training, Dataset curation, or Model architecture. - One baseline attempt per contractor per task. - Each task may only be attempted once. - All work is confidential and covered by NDA. - Compute and environment are provided; no personal GPU required. Application Process - Upload resume - AI interview based on your resume - Submit form Resources & Support - For details about the interview process and platform information, please check: interview process details . - For any help or support, reach out to: support@mercor.com . - Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.