Research Engineer
Building the infrastructure that tells you when AI is wrong - and working to understand why.
About
I design and evaluate AI systems with a focus on measurable safety, reliability, and bias reduction.
Current focus
Making AI measurably safer through better evaluation, observability, and bias mitigation.
I build evaluation frameworks and observability infrastructure for deployed AI systems. At Arva AI, I redesigned benchmarking pipelines and built end-to-end agentic tracing - replacing unstructured logs with structured visibility across the full agent lifecycle.
My research background is in clinical AI - specifically demographic bias in pediatric mental health records. That work, developed during two years as a Research Assistant at Queen Mary University of London, was published in Nature (2026).
Open to research roles in AI safety, evaluation, and interpretability. Also building toward AI governance, supported by AI Governance coursework from Saïd Business School, University of Oxford.
Research & Publications
Selected work on clinical AI bias, evaluation methodology, and research communication.
Communications Medicine (Nature Portfolio) · March 2026
Achieved a 27% reduction in diagnostic bias and a 4% accuracy gain for female patients through neutralised language and data-centric mitigation. The work improves diagnostic equity in pediatric mental health AI.
Privacy-Preserving Behaviour of Chatbot Users: Steering Through Trust Dynamics
arXiv · November 2024
Traceability Solution for SMEs
IJSREM · June 2021
Deep Neural Network Compiler
IRISS 2020 · March 2020
Selected Talks & Presentations
2024Poster - AI4H Conference, Italy - Bias mitigation for pediatric EHR notes
2024Alan Turing Data Science Conference - EHR bias research
Experience
Roles focused on AI evaluation infrastructure, safety research, and applied model quality.
May 2026 / Present
Mercor Intelligence · Remote (Contract)
Mar 2026 / April 2026
BlueDot Impact · Remote
Dec 2025 / Mid Mar 2026
Arva AI · London
Mar 2025 / Nov 2025
Mercor Intelligence · Remote
Nov 2023 / Feb 2025
Queen Mary University of London · London
Earlier
Product Engineer, AI Technology · AI Technology & Systems · California (Remote)
Built a Deep Neural Network Compiler using Eigen, ONNX, and Caffe for edge devices. Supervised 45 interns building TinyML applications.
Jul 2021 / Jul 2022
Research Intern · Indian Institute of Technology · Indore
Developed AR applications and a traceability app for Android - published. Contributed to an Intelligent AGV for smart manufacturing (Industry 4.0).
Nov 2019 / Mar 2021
Projects
Practical builds spanning evaluation tooling, multimodal AI, and production-oriented research systems.
FeaturedIndependent Research
Version-tracking bias evaluation pipeline across 6 LLMs (Llama 2-3.2, Gemini 2.5-3.1) using 400 synthetic clinical vignettes derived from MIMIC-IV. Found a 4.4x reduction in demographic pain-score disparity from a prompt wording change alone.
FeaturedIn progress
End-to-end pipeline for detecting deepfake and AI-generated speaking videos on YouTube. Tests motion entropy as a computationally efficient signal alongside traditional classifiers - with a Claude-powered reporting layer and Streamlit human review dashboard.
FeaturedHackathon
AI-powered platform connecting researchers with career-defining opportunities. Built at the Perplexity Hackathon - uses grounded search and LLM reasoning to match researchers with roles via a reasoning graph over papers, skills, and opportunities.
Vedic Mathematics for AI Acceleration
Four-phase empirical study (Python → C++ → CUDA on RTX 3050 Ti → transformer attention) testing whether the Urdhva-Tiryagbhyam Vedic multiplication sutra offers ML acceleration advantages. Found a Vedic-inspired attention tiling strategy with 16x more concurrent computation and 19% lower memory traffic than FlashAttention for edge inference.
2026
Multimodal Hate Speech Detection
Compared early, late, and cross-attention fusion across BERT, ViT, and VisualBERT for multimodal classification.
May - Sep 2023
Article Person Verification Agent
Autonomous adverse media screening using LangGraph and Gemini - with MLflow tracing and multilingual support across 6+ scripts.
2025
Brain Tumor Segmentation
3D FMRI segmentation using NVIDIA deep learning libraries and SAM.
Sep - Dec 2023
Traceability App for SMEs
End-to-end supply chain traceability; launched on Google Play Store.
2021
DNN Compiler for Edge Devices
Compiled high-level DNN specs to optimised machine code for constrained hardware using C++ and Eigen.
2020
Skills
Core stack for building, evaluating, and shipping reliable AI systems.
AI & Research
Evaluation Frameworks, Bias Mitigation, Benchmarking, Agentic AI, RAG, RLHF, Explainability (LIME, GradCAM), Generative AI, Deep Learning, NLP, Computer Vision, Reinforcement Learning
Infra & MLOps
Langfuse, MLFlow, Vertex AI, AWS, Azure, Terraform, CUDA, GPU Optimisation, Docker
Languages
Python, C/C++, SQL, TypeScript, Bash, R
Frameworks
PyTorch, TensorFlow, HuggingFace, Scikit-Learn, SpaCy, OpenCV, NumPy, Pandas, Playwright
Databases
PostgreSQL, MongoDB, Firebase, Redis
Contact
I'm open to research collaborations, full-time roles in AI safety and evaluation, and conversations about the field. Best reached by email.
visdav8@gmail.com