Research Engineer

• Write and maintain Python ML code
• Research, prototype, and develop new materials-aware ML models and features
• Collaborate closely with other engineers, frequently reviewing code and best practices
• Mentor other developers
• Design high-performance, scalable ML systems
• Test and analyze the impact and performance of models
• Work in a multifunctional team on features from concept to delivery
• Improve and maintain core Python libraries enabling machine learning for materials science problems at industrial scale
• Collaborate with the Product team to shape AI capabilities and support customer use cases
• Improve machine learning model interpretability and develop user communication tools
• Improve inverse design capabilities for complex materials synthesis parameter spaces
• Collaborate with the External Research and Development team to write and publish papers
• Develop new methods to quantify uncertainty in machine learning predictions

Data & Artificial Intelligence Research Engineer, L4

• 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

• Lead vulnerability research and reverse engineering efforts aligned with customer objectives
• Define research strategies, assess technical feasibility, and guide teams through project planning, kickoff, execution, and delivery
• Perform advanced static and dynamic analysis using disassemblers, debuggers, emulators, tracers, and fuzzers
• Identify vulnerabilities, conduct root-cause analysis, and document reproducible findings
• Develop reliable proof-of-concept exploits and operational exploit capabilities for authorized customer requirements
• Evaluate and bypass modern exploit mitigations and security mechanisms as part of authorized research
• Build tooling, automation, and research infrastructure that improve vulnerability discovery, analysis, and reproducibility
• Communicate research progress, technical risks, findings, and recommendations to technical and non-technical stakeholders
• Share research findings internally and externally when authorized and appropriate
• Mentor security researchers, review technical work, and promote rigorous research and documentation practices
• Lead small to medium-sized research teams, maintaining technical quality and alignment with project objectives

Senior Reverse Engineering/Vulnerability Research Engineer

• Conduct hypothesis-driven proactive threat hunting across Stripe systems and external data
• Analyze adversary behavior and complex fraud vectors using Stripe’s FT3 taxonomy
• Integrate, curate, and automate threat intelligence feeds into engineering workflows
• Translate research findings into threat advisories and strategic control recommendations
• Simulate adversary tactics, techniques, and procedures using agentic automated testing frameworks
• Validate deployed controls and generate regression scenarios
• Collaborate with Fraud Ops, Strategy, Risk, Onboarding, Security, and Fraud Intelligence teams
• Identify and systematically eliminate product vulnerabilities

Abuse Research Engineer

• 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

• Build the component layer around the layout synthesis engine, including a documented API contract, callable service, and evaluation gate
• Turn model retraining into a one-command job with built-in benchmarks and readable team results
• Own latency and cost budgets for learned capabilities
• Extend core research into capabilities such as multi-story plans and real-time editing
• Partner with infrastructure engineers on MLOps and deployment
• Mentor researchers and interns on engineering practices
• Communicate work through weekly status updates, clear pull requests, and design notes
• Ship spatial AI research capabilities into Higharc's home-design and construction product
• Improve the existing engine, schema, and evaluation frameworks for speed, reproducibility, and extensibility

Applied 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
• Develop datasets and evaluations for training and evaluating embodied AI systems

Research Engineer, Robotics Data

• Design, implement, and train causal graph models and planning algorithms
• Architect approaches to multi-objective optimization
• Build and maintain training and inference pipelines
• Manage experiment tracking, model versioning, dataset loaders, and objectives and interfaces
• Package trained models as containerized inference components conforming to specified APIs
• Work with the Lead System Integrator on regular code drops and major code revisions
• Contribute to synthetic data generation and simulation campaigns
• Present research results at program design reviews, site visits, and PI workshops
• Author technical sections of monthly status reports and design documentation
• Coordinate with academic subcontractor researchers on shared model components

AI/ML Research Engineer

• Support Ford’s product safety investigations process within the Pre-Investigation Review team or Vehicle Hardware Critical Concern Review Group
• Help determine whether issues require further review based on occupant and road-user effects, compliance considerations, occurrence considerations, and other factors
• Support investigations evaluating potential safety defects or regulatory non-compliance related to vehicle hardware systems
• Lead collaboration with Product Development, Ford Customer Service Division, Supplier Technical Assistance, Manufacturing, and Office of General Counsel
• Develop company contacts to support timely root-cause ownership identification
• Coordinate special tests and field surveys to assess facts in safety and compliance evaluations
• Prepare presentations and reports, including engineering analysis and special vehicle/component demonstrations
• Create and maintain investigation documentation and submit regulatory documentation as necessary
• Maintain trackers, dashboards, and status reports across concurrent safety-defect and compliance investigations

Safety Investigative Engineer

• Drive operational excellence through AI-native tools and workflows across the organization
• Partner with VPs, directors, sales leadership, subject matter experts, and senior leaders
• Architect and build proprietary AI agents and automation workflows using company-specific data
• Develop and manage a playbook covering discovery, pilots, improvement, implementation, adoption, and measurement
• Lead pilots and proofs of concept across multiple disciplines
• Develop and maintain AI-tool, business-system, and advertising-platform integrations
• Build APIs, validation frameworks, and data pipelines
• Collaborate with subject matter experts to ensure workflows are valuable, usable, and connected to appropriate data
• Establish systems and cadences to improve adoption, check outputs, measure progress, and revisit training
• Own documentation of the AI stack, including tool inventory, vendor strategy, build-vs-buy posture, integration map, and shared-context architecture
• Develop reusable patterns, agentic workflows, and MCP integrations
• Consult on build-versus-buy decisions
• Run internal training, office hours, and troubleshooting clinics
• Advise on service-model artifacts, scope-of-work content, pricing implications, policies, and marketing collateral
• Contribute to AI policies and SOPs that mitigate risk
• Catalogue workflows, skills, and AI tools
• Integrate workflows into a custom frontend with the Head of Apollo and engineering resources
• Support growth of the innovation function as a center of excellence or extension of the Data Platform, Apollo

Research Engineer jobs in United States