• own the team’s research infrastructure and overnight pipelines • work closely with both researchers on the team and engineers who oversee trading execution system • help optimize machine learning training and testing environments • architect and maintain data pipelines and job graphs • help deploy new machine learning driven market making strategies to production • mentor and develop other engineers on the team and share your practices and knowledge

• Lead the 0-1 development of the User Identity Knowledge Graph • Identify and articulate the key challenges and opportunities in modeling user knowledge at scale • Design, prototype, and deploy novel machine learning and knowledge representation models • Prioritize and sequence research efforts, balancing long-term vision with near-term impact • Collaborate with data scientists, engineers, product managers, and stakeholders across Netflix

Role Description VHA supports and provides medical care for VA’s eligible beneficiaries through the VA health program, which includes VA Medical Centers (VAMCs) and contracted health care networks. The Office of Clinical Informatics within the Office of Health Informatics (OHI), Digital Health Office (DHO) advances the enterprise standard of care and patient experience using improved data, tools, and informatics processes organized around continuously delivering value to its customers. This is achieved through Lean-Agile delivery of clinical practice solutions that support best practice standards for clinical care. OHI is further responsible for ensuring the success of the modernized VA EHR, Oracle Health Millennium/Cerner through continuous exploration, integration, deployment and release on demand of Integrated Health Technology (IHT) solutions. These efforts aim to increase Veterans' access to care and support their active participation in their healthcare. Gritter Francona is looking for a Lead Human Factors Engineer to help support a potential project to assist this objective. Key Responsibilities - Strategy & Leadership - Serve as the HFE lead for the contract, owning the HFE strategy, roadmap, and quality bar for deliverables; establish HFE governance, definitions of ready/done, templates, and review gates. - Translate state‑of‑the‑art research and industry trends in human factors, cognitive/organizational psychology, and clinical informatics into scalable methods and guidance for teams. - Champion Lean‑Agile principles within a SAFe® environment, shaping ART ceremonies (PI Planning, System Demo, Inspect & Adapt) to drive value delivery and continuous improvement. - Research & Evaluation - Plan and execute mixed‑methods research (contextual inquiry, cognitive task analysis, diary studies, think‑aloud protocols, surveys, card sorting, tree testing, formative/summative usability) tailored to risk and regulatory context. - Design and conduct workflow analyses across clinical informatics domains (e.g., order entry, medication administration, care coordination, documentation, decision support) to identify latent conditions, handoff risks, and transient failure modes that evade unit testing. - Apply advanced risk and resilience methods (e.g., FMEA/FMECA, STPA, bow‑tie analysis, FRAM, HFACS) to anticipate and mitigate human‑system hazards. - Design & Systems Impact - Visualize and articulate system‑wide impacts of design changes on all user groups (end users, supervisors, support staff, patients, administrators). - Recommend redesigns or operational mitigations to minimize negative outcomes when changes are constrained or products/processes are essential. - Collaborate with product, engineering, clinical SMEs, and data teams to prototype designs (low‑ to high‑fidelity) and evaluate effects on safety, workflow, workload, and adoption. - Live Interaction & Enablement - Provide thoughtful, actionable suggestions during live demo calls; adapt in real time to participant feedback in user sessions of any type (moderated, unmoderated, remote, in‑person). - Mentor cross‑functional teams in HFE practices; deliver trainings and brown‑bag sessions to empower high‑performing Agile teams. - Measurement, Quality & Compliance - Define and track HFE KPIs (e.g., task success, time‑on‑task, error rates, cognitive load, adoption, defect escape rate, alert fatigue indices). - Produce clear, standards‑aligned HFE documentation (protocols, moderation guides, test plans, risk analyses, traceability matrices, usability engineering files, summative reports). - Ensure alignment with applicable standards and guidance. Qualifications - Masters' degree - 10+ years of experience in human factors engineering, with a strong record leading research, evaluations, and design for complex, safety‑critical or high‑reliability systems. - Experience applying SAFe® Lean‑Agile principles and practices to drive value, sustain organizational change, and enable high‑performing Agile teams. Benefits - Health Care Plan (Medical, Dental & Vision) - Retirement Plan (401k, IRA) - Life Insurance (Basic, Voluntary & AD&D) - Paid Time Off (Vacation, Sick & Public Holidays) - Short Term & Long Term Disability - Training & Development

• Architect and validate rigorous Continual Pre-Training (CPT) frameworks, focusing on domain adaptation techniques that effectively transfer Reddit’s knowledge into licensed frontier models. • Design the "Science of Multimodality": Lead research into fusing vision and language encoders to process Reddit’s rich media (images, video) alongside conversational text threads. • Formulate data curriculum strategies: scientifically determining the optimal ratio of "Reddit data" vs. "General data" to maximize community understanding while maintaining safety and reasoning capabilities. • Conduct deep-dive research into Scaling Laws for Graph-based data: investigating how Reddit’s tree-structured conversations impact model convergence compared to flat text. • Design and scale continuous evaluation pipelines (the "Reddit Gym") that monitor model reasoning and safety capabilities in real-time, enabling dynamic adjustments to training recipes. • Drive high-stakes architectural decisions regarding compute allocation, distributed training strategies (3D parallelism), and checkpointing mechanisms on AWS Trainium/Nova clusters. • Serve as a force multiplier for the engineering team by setting coding standards, conducting high-level design reviews, and mentoring senior engineers on distributed systems and ML fundamentals.