Gramian Consulting

AI Engineer

• Review and evaluate AI-generated code across programming languages and software engineering scenarios.
• Assess code for correctness, reliability, security, scalability, readability, and maintainability.
• Identify bugs, logical errors, incomplete implementations, edge-case failures, and architectural weaknesses.
• Analyze unfamiliar codebases and understand the impact of proposed changes.
• Review bug fixes, feature implementations, refactoring, API integrations, configuration changes, and database operations.
• Compare alternative implementations and determine whether they satisfy technical requirements.
• Rewrite or improve code to create high-quality reference solutions.
• Provide clear technical explanations of identified issues and recommended improvements.
• Develop evaluation criteria, technical annotations, and rubrics for code-quality assessment.
• Collaborate with researchers and engineering teams to design coding benchmarks and improve LLM evaluation methodologies.

Senior Software Engineer – AI Code Evaluation

• Review and evaluate AI-generated code across programming languages and software engineering scenarios
• Assess code for correctness, reliability, security, scalability, readability, and maintainability
• Identify bugs, logical errors, incomplete implementations, edge-case failures, and architectural weaknesses
• Analyze unfamiliar codebases and understand the impact of proposed changes
• Review bug fixes, feature implementations, refactoring, API integrations, configuration changes, and database operations
• Compare alternative implementations and determine whether they satisfy technical requirements
• Rewrite or improve code to create high-quality reference solutions
• Provide clear technical explanations of identified issues and recommended improvements
• Develop evaluation criteria, technical annotations, and rubrics for code-quality assessment
• Collaborate with researchers and engineering teams to design coding benchmarks and improve LLM evaluation methodologies

AI Research Scientist

• Investigate the capabilities, limitations, and training methods of frontier AI systems.
• Formulate research questions that inform AI products, platforms, and technical strategy.
• Explore approaches to synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation.
• Stay current with advances in machine learning and identify opportunities for technical contributions.
• Develop research-grade datasets, experiments, prototypes, tooling, and evaluation frameworks.
• Train, test, and evaluate models using modern AI and machine learning tools.
• Analyze experimental results and develop evidence-based conclusions.
• Establish practices for data quality, reproducibility, experimental design, and evaluation.
• Iterate from research hypotheses to validated technical insights.
• Collaborate with Research, Engineering, Product, and Operations teams to translate findings into practical applications.
• Communicate technical findings to specialized and cross-functional audiences.
• Contribute to technical reports, publications, open-source projects, workshops, or conferences where appropriate.
• Mentor engineers and researchers and contribute to technical discussions and peer review.

Staff Research Engineer – AI, Machine Learning

• Investigate the capabilities, limitations, and training methods of frontier AI systems.
• Formulate research questions informing AI products, platforms, and technical strategy.
• Explore synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation.
• Stay current with machine learning advances and identify meaningful technical contributions.
• Develop research-grade datasets, experiments, prototypes, tooling, and evaluation frameworks.
• Train, test, and evaluate models using modern AI and machine learning tools.
• Analyze experimental results and develop evidence-based conclusions.
• Establish rigorous practices for data quality, reproducibility, experimental design, and evaluation.
• Iterate from research hypotheses to validated technical insights.
• Collaborate with Research, Engineering, Product, and Operations teams to translate findings into practical applications.
• Communicate technical findings to specialized and cross-functional audiences.
• Contribute to technical reports, publications, open-source projects, workshops, or conferences where appropriate.
• Mentor engineers and researchers and contribute to technical discussions and peer review.

Staff Research Engineer – AI & Machine Learning

• Investigate the capabilities, limitations, and training methods of frontier AI systems
• Formulate research questions that inform AI products, platforms, and technical strategy
• Explore synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation
• Stay current with advances in machine learning and identify opportunities for technical contributions
• Develop research-grade datasets, experiments, prototypes, tooling, and evaluation frameworks
• Train, test, and evaluate models using modern AI and machine learning tools
• Analyze experimental results and develop evidence-based conclusions
• Establish practices for data quality, reproducibility, experimental design, and evaluation
• Iterate from research hypotheses to validated technical insights
• Collaborate with Research, Engineering, Product, and Operations teams to translate findings into practical applications
• Communicate technical findings to specialized and cross-functional audiences
• Contribute to technical reports, publications, open-source projects, workshops, or conferences where appropriate
• Mentor engineers and researchers and contribute to technical discussions and peer review

• Own end-to-end program delivery across scope, timelines, quality, throughput, contributor performance, and cost.
• Design and manage workflows for coding datasets, agentic trajectories, RL environments, benchmarks, and rubric-based evaluations.
• Identify operational bottlenecks and improve workflows through better instructions, sequencing, incentives, review systems, and capacity planning.
• Define contributor requirements and partner with talent teams to source, assess, onboard, train, and ramp distributed software engineers.
• Build team-lead and reviewer structures for programs involving 100–1,000+ contributors.
• Own quality-control systems and analyze datasets to identify trends, systematic errors, and root causes.
• Act as a primary customer contact for AI labs, communicating progress, risks, quality trends, and recovery plans.
• Translate research objectives into practical task specifications and challenge requirements when they may not produce the intended evaluation signal.
• Use Python, SQL, or similar tools to automate quality sampling, defect analysis, throughput reporting, and operational reviews.
• Convert successful workflows into reusable playbooks, quality controls, evaluation assets, and contributor-management systems.
• Share operational learnings and mentor other program leads.

Technical Program Operations Lead – AI Engineering

Engineer

• Perform and certify complex aircraft maintenance activities
• Coordinate on-site technical interventions
• Ensure compliance with strict aviation safety and quality standards
• Support international customer assignments and worldwide missions
• Join an international technical operations team

Aircraft Maintenance Engineer – EASA B1

General

• Support enterprise cryptography, encryption, PKI, and key management initiatives
• Assess cryptographic controls and identify security and compliance gaps
• Support the design, implementation, and improvement of enterprise key management processes
• Work with Hardware Security Modules (HSMs) and enterprise key management platforms
• Review encryption algorithms, digital certificates, TLS, PKI, and tokenization controls
• Develop and maintain cryptographic standards, policies, and security requirements
• Support remediation activities related to cryptographic risks and control deficiencies
• Collaborate with security architecture, infrastructure, application, and compliance teams
• Support cryptographic transformation, crypto-agility, encryption modernization, and large-scale key management programs
• Help ensure cryptographic solutions align with regulatory requirements and industry best practices

Enterprise Cryptography, Key Management Specialist

Artificial Intelligence

• Translate authentic Earth-science workflows into self-contained terminal-based benchmark tasks.
• Prepare and structure geospatial, climate, atmospheric, geological, hydrological, or oceanographic datasets.
• Build reproducible computational environments using scientific libraries and command-line tools.
• Develop expert reference solutions using Python, R, Bash, Julia, or domain-specific software.
• Design tasks involving geospatial analysis, time-series processing, numerical modeling, interpolation, forecasting, remote sensing, and environmental risk analysis.
• Define objective grading criteria for scientific outputs, data transformations, and spatial or temporal accuracy.
• Validate coordinate systems, units, timestamps, missing-data handling, and scientific assumptions.
• Create automated tests for numerical tolerances, file formats, metadata, and reproducibility.
• Debug issues involving projections, large datasets, dependencies, performance, and numerical stability.
• Document data provenance, expected outputs, edge cases, assumptions, and limitations.

Earth & Environmental Science Expert – AI Projects

• Participate in technical discovery sessions with client R&D teams.
• Map and deconstruct end-to-end materials discovery workflows into discrete subprocesses.
• Convert real-world materials engineering challenges into structured test scenarios.
• Define inputs, constraints, expected outputs, and verified golden reference solutions.
• Develop scientific scoring rubrics and programmatic validation rules.
• Validate criteria such as stoichiometry, thermodynamics, and simulation stability.
• Inspect step-by-step AI reasoning traces to identify failure patterns and root causes.
• Distinguish scientific errors from incorrect assumptions, implementation issues, or evaluation defects.
• Define domain-specific data generation requirements and synthetic physics pipelines.
• Contribute to discussions on fine-tuning strategies and methods for improving AI model performance.
• Communicate technical findings and recommendations during client workshops.

Materials Science, Semiconductor AI Expert

• Create realistic, challenging coding tasks involving feature implementation and software bug fixes.
• Design tasks that reflect real-world backend engineering scenarios.
• Develop deterministic verifiers that accept reasonable valid solutions and reject incorrect outputs.
• Work with backend technologies such as Java, Node.js, Go, Rust, TypeScript, Python, C++, or similar languages.
• Evaluate code for correctness, reliability, maintainability, and performance.
• Incorporate automated testing and clear validation criteria into coding tasks.
• Identify and account for edge cases, performance bottlenecks, and potential implementation errors.
• Apply secure coding principles and cybersecurity considerations where relevant.
• Refine tasks and verification logic based on project requirements and quality standards.

Gramian Consulting

Senior Software Engineer – AI Code Evaluation

Senior Software Engineer – AI Code Evaluation

Staff Research Engineer – AI, Machine Learning

Staff Research Engineer – AI & Machine Learning

Staff Research Engineer – AI & Machine Learning

Technical Program Operations Lead – AI Engineering

Aircraft Maintenance Engineer – EASA B1

Technical Program Operations Lead – AI Engineering

Technical Program Operations Lead – AI Engineering

Enterprise Cryptography, Key Management Specialist

Earth & Environmental Science Expert – AI Projects

Earth & Environmental Science Expert – AI Projects

Materials Science, Semiconductor AI Expert

Backend Software Engineer – AI Training Projects

Backend Software Engineer – AI Training Projects