Research Engineer

• Research data needs of robot learning and physical AI systems and turn them into dataset and evaluation specifications
• Define data schemas, annotations, ground truth, and quality standards across robotics data types
• Design collection and review protocols for external data providers
• Build tools and validation workflows to audit datasets, identify quality issues, and provide actionable feedback
• Run experiments and analyze model behavior to understand how data quality, coverage, and structure affect performance
• Collaborate with HUD’s research and engineering teams, data vendors, and buyers to improve robotics data offerings
• Participate in two technical interviews and a 2–3 day work trial during the hiring process

Research Engineer, Robotics Evals

• Design and run head-to-head benchmarks of data providers against verified ground truth
• Build and maintain test datasets and ground truth, preventing staleness and leakage
• Own an automated, reproducible, versioned, and defensible benchmark harness and weekly release
• Measure accuracy, coverage, freshness, speed, and cost
• Work with marketing to publish public leaderboard pages
• Feed benchmark results into Alexandria's provider-selection system
• Expand benchmark categories within Alexandria and beyond
• Publish rigorous automated public benchmarks weekly

Research Engineer – Benchmarks

• Research data needs of robot learning and physical AI systems and turn them into dataset and evaluation specifications
• Define data schemas, annotations, ground truth, and quality standards across robotics data types
• Design collection and review protocols for external data providers
• Build tools and validation workflows to audit datasets, identify quality issues, and provide actionable feedback
• Run experiments and analyze model behavior to understand how data quality, coverage, and structure affect performance
• Collaborate with HUD’s research and engineering teams, data vendors, and buyers to improve robotics data offerings
• Develop datasets and evaluations for training and evaluating embodied AI systems

Research Engineer, Robotics Data

• Build industry-leading healthcare AI models and model pipelines powering voice agents and the patient platform
• Own large initiatives advancing model and agent capabilities in healthcare while maintaining reliability, efficiency, and precision
• Lead research and engineering to improve conversational capabilities, including instruction following, tool calling, retrieval, and memory
• Investigate speech and audio research questions and design experiments to test model hypotheses
• Build and iterate end-to-end models and pipelines optimized for quality, efficiency, and user experience
• Identify model-task optimization opportunities based on performance, latency, and cost
• Partner with platform and product engineers to integrate new models into production systems
• Convert ambiguous research ideas into iterative milestones and roadmaps
• Train, fine-tune, validate, and further develop in-house and customer-developed models
• Design evaluation protocols and benchmarks measuring quality, generalization, robustness, and research progress
• Analyze datasets, model outputs, and failure cases to guide model development

• Build high-quality benchmarks for evaluating frontier agents on domain-specific tasks
• Own the design, implementation, and quality of HUD’s internal agent benchmarks
• Work with subject-matter experts to define tasks and create domain-specific benchmarks evaluating realistic workflows
• Build infrastructure to reliably run models and agents against benchmark tasks
• Develop metrics and analyses to understand benchmark difficulty, reliability, and failure modes
• Validate whether benchmark performance correlates with real-world evaluations, customer needs, and lab expectations
• Write clear documentation and benchmark reports for technical audiences
• Participate in a 2–3 day work trial during the hiring process

• Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information and design transformations based on data type and downstream use case
• Develop and benchmark detection approaches combining rules, statistical models, classifiers, and LLM-based methods
• Build production pipelines that anonymize raw data before downstream processing, training, evaluation, or synthetic data generation
• Create evaluation frameworks measuring privacy risk and retained data utility, including recall-weighted metrics, leakage tests, and adversarial re-identification attempts
• Design systems robust to new data sources, schema drift, unusual formats, and sensitive information embedded in unexpected fields
• Work with engineering, research, operations, and customers to translate privacy requirements into practical technical policies and safeguards
• Own the full pipeline for protecting privacy without destroying the structure and signal valuable for AI-agent training
• Participate in a 2–3 day work trial during the application process

Research Engineer, Privacy and Anonymization

• Build HUD’s synthetic data pipeline for RL training data and evals for frontier AI agents
• Work with subject-matter experts to create synthetic tasks across professional and technical domains
• Design methods that generate diverse, realistic, and learnable synthetic tasks
• Build systems and tooling to mutate, validate, and improve synthetic tasks
• Analyze model and agent performance to understand task learning outcomes and failure modes
• Develop metrics for synthetic task diversity, realism, and learnability
• Complete a process involving two technical interviews and a 2–3 day work trial

Research Engineer, Synthetic Data

• Build systems to automate quality control for training data created using HUD’s infrastructure
• Create QC systems based on true understanding and human judgment, without heavy reliance on LLMs
• Define and enforce quality standards for training data
• Design experiments and metrics to grade agent outputs
• Partner with data vendors to debug quality issues and diagnose agent failure modes
• Provide actionable feedback and improve vendor data-generation processes
• Translate QC learnings into supplier-dataset auditing systems, including sampling strategies, validation pipelines, and feedback loops
• Integrate QC learnings into infrastructure tools and the data vendor portal to reduce anomalies, inconsistencies, and edge cases
• Participate in a process involving two technical interviews and a 2–3 day work trial

Research Engineer, QC Automation

• Own technical deployment requests from frontier AI labs, data vendors, and internal teams from triage to completion
• Clarify ambiguous requests and identify the actual work required
• Build tools and one-off pipelines for urgent customer or partner problems
• Coordinate with research and go-to-market teams to unblock deployments
• Balance speed and quality when customers need fast turnaround and requirements are not fully specified
• Document recurring issues and convert repeated manual work into reusable tools or processes
• Work closely with frontier AI labs and data vendors
• Complete a 2–3 day work trial as part of the hiring process

Forward Deployed Research Engineer

• Monitor and analyze sophisticated cyber threats targeting Anduril's products, infrastructure, and personnel
• Research, mitigate, and anticipate emerging technical trends in the threat landscape
• Collaborate with the detection and response team to provide timely, actionable intelligence supporting investigations
• Engage cross-functionally with the offensive security team on product and infrastructure red team engagements
• Enhance tooling for threat actor tracking and intelligence data integration
• Foster proactive collaboration and information exchange with external partners to build joint capabilities and achieve shared objectives

Research Engineer jobs in California

Research Engineer, Robotics Evals

Research Engineer – Benchmarks

Research Engineer, Robotics Data