Role Description - Author and review challenging physics problems to train and evaluate frontier AI models. - Evaluate model outputs on tasks like analytical derivation, mechanism prediction, and numerical modeling. - Identify areas where current models fail, especially mistakes a working PhD physicist would catch. - Collaborate with a small program team and other senior contributors. Qualifications - First-author or co-first-author publications in journals like Physical Review Letters, Nature, or Science. - Doctoral or postdoctoral training at a top-20 global institution for physics or STEM. - Competitive fellowships such as NSF GRFP, Hertz, or DOE Computational Science Graduate Fellowship. - Issued patents, spin-out companies, or grants as a named PI or co-PI. - International Physics Olympiad (IPhO) Gold/Silver/Bronze medalist. - Currently or recently in an active research role: PhD candidate, postdoc, instructor, faculty, principal scientist, or industry researcher. Requirements - W-2 employment with Cincinnatus LLC; payroll, benefits, and compliance handled by Cincinnatus. - Compensation reflects research seniority and credentials. - No fixed end date; contributors continue as long as the program needs their expertise. 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: https://talent.docs.mercor.com/welcome - 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.

Role Description Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey. Position: MLOps Engineer Expert Type: Contract Compensation: $90–$140/hour Location: Remote Commitment: 40 hours/week Role Responsibilities - Guide research and engineering teams to close knowledge gaps and improve AI model performance in MLOps, training infrastructure, and ML framework-level topics. - Design challenging, domain-relevant tasks, and write accurate and well-structured solutions to MLOps and ML systems problems. - Evaluate MLOps tasks and solutions and provide clear, written technical feedback. - Develop guidelines and detailed rubrics/evaluation frameworks to assess training pipeline design, distributed systems reasoning, and kernel-level optimization across tasks. - Collaborate with other subject matter experts to ensure consistency and accuracy in training data. Qualifications - Must-Have: 2+ years of dedicated professional experience in ML infrastructure, MLOps, or ML systems engineering at a recognized, top-tier organization. - Hands-on production experience with JAX and/or PyTorch at scale. - Experience writing or optimizing custom GPU kernels using Pallas (JAX) or Triton. - Demonstrable career progression. - Ability to engage reliably for at least 40 hours/week during weekdays. - Strong written communication skills and the ability to explain complex technical decisions clearly. 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 - For any help or support, reach out to: support@mercor.com - PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.

• Design and build multi-agent orchestration patterns (master-orchestrator + specialized worker agents) using protocols such as MCP and A2A • Lead end-to-end delivery of agentic underwriting and claims automations, from prototype through production hardening • Partner with IE on orchestration entry-point design (e.g., Azure Function endpoints, master-agent gateways) so the AE/IE seam is clean and scalable • Lead offshore engineers and team members on agentic patterns, prompt engineering, and reliability practices • Establish coding, evaluation, and observability standards for agentic systems within the AI & Automation Center of Excellence • Translate business intent into working agentic systems — not just systems that compile, but systems that deliver measurable business outcomes.

• Own an agentic-enabled client support experience end-to-end - from first contact through resolution - ensuring every user interaction reflects Titan's standard of excellence • Deepen client adoption of the Titan platform through hands-on guidance on prompting, workflows, and use case development • Synthesize client feedback into structured, high-signal inputs for Titan's Product and Engineering teams • Investigate platform issues with depth and rigor, exhausting resolution paths before escalating to Engineering, Product or Forward Deployed Engineering • Develop sharp pattern recognition across inbound requests - distinguishing user education gaps, product limitations, and technical issues in real time • Build and continuously improve a living knowledge base that makes the entire team smarter and faster over time • Maintain visibility across client communication channels, ensuring nothing slips and no client feels unheard • Team with Client Partners and Forward Deployed Engineers, enabling seamless handoffs and a unified client experience