Vetted Machine Learning Professionals

“Entrepreneurial builder and former founding CTO of a Y Combinator startup that was acquired. Now in the planning stage for a new venture (considering edtech, media creation, and food/"intelligent servicing"), emphasizing early design partners, rapid testing, and iteration to validate ideas.”

“25+ year professional exploring entrepreneurship; previously helped develop corporate venture capital at Siemens and AVEVA and ran end-to-end diligence through deal close, with heavy technical diligence on pre-seed/pre-revenue companies. Interested in building an AI-enabled hard lending/origination approach to reduce decision-to-close time and scale in a large lending market, and prefers VC studio/EIR models with paid roles rather than equity-only arrangements.”

Tricia Biggio

Screened

Executive media and creative-tech leader specializing in entertainment and AI storytelling

Los Angeles, CA18y exp

“Former co-founder/CEO of a venture-backed media-tech startup who spent 6 years raising capital, leading through market shifts, and building with major investor backing. She brings strong relationships across venture studios and senior executives in media, entertainment, consumer products, startups, and AI, and led her last company into Amazon's AWS GENAI accelerator as one of 15 selected in North America.”

“Engineering leader who built and delivered an IoT smart-spaces platform for the self-storage and smart-living domains, translating customer requirements into architecture, capability maps, and a multi-milestone roadmap. Personally stood up missing AI/ML capabilities (including churn prediction) using Databricks (Delta Lake/MLflow), enabling follow-on features like energy optimization and security/anomaly detection. Scaled an org from 20 to 80+ with disciplined Agile planning (Jira Advanced Roadmaps/Confluence) and strong executive/customer-facing leadership during high-stakes customer commitments.”

“Player-coach engineering leader focused on scalable data security scanning and risk detection in hybrid cloud, owning architecture and core implementation of an incremental/parallel DSPM scanning engine. Shipped production improvements including 60% lower scan latency and 30% fewer false positives, with strong emphasis on correctness under concurrency, multi-tenant observability (SLOs/burn-rate alerts), and disciplined rollout practices (feature flags, shadow scans, canaries).”

Parth Vadera

Screened

Senior AI/ML Data Scientist specializing in recommender systems, LLMs, and MLOps

Tracy, CA11y exp

“ML/NLP leader with 12+ years of impact across LinkedIn, TikTok, and Levi's, building and productionizing multimodal recommendation and embedding-based search systems. Deep experience in entity resolution, vector retrieval, and rigorous evaluation, with cloud-native deployment/monitoring (MLflow, Airflow, SageMaker/Lambda, Azure ML, Kubernetes) and demonstrated double-digit relevance gains at millions-of-users scale.”

Dennis Noto

Screened

Executive Technology & Security Leader specializing in FinTech, AI platforms, and enterprise modernization

Denver Metro Area36y exp

“Technology transformation leader who builds board-approved roadmaps and scales engineering orgs with strong Agile execution. Led large modernization efforts (e.g., Scottrade: 3,000 programs/4M LOC in 18 months) and scaled POCs into enterprise SaaS platforms using Docker, Kubernetes, Helm, and Terraform for high-concurrency workloads.”

Shant Mardigian

Screened

Executive Engineering Leader specializing in scalable streaming, media supply chain, and AI operations

19y exp

“Tech executive with Disney experience who has repeatedly scaled and restructured engineering organizations (from 4 to 30 and up to 100+), using OKRs/KPIs to drive business-aligned roadmaps. Hands-on with architecture and platform strategy, including adopting MongoDB Atlas to centralize transactional data and building shared core services (security/permissions, auditing, compliance) to increase product velocity across distributed teams.”

“Engineering leader (ActiveCampaign, Yalo) with a track record of scaling both systems and orgs: grew an engineering team from 90+ to 200+ (30+ scrum teams) while re-architecting a marketing automation platform from batch to near real-time. Led major infrastructure shifts (RabbitMQ to Kafka, multi-region redundancy) and reports outcomes including 600%+ throughput gains, 99.99% uptime, and business growth from ~80K to 185K customers with revenue surpassing $200M over ~3 years.”

Alexander Schaab

Screened

Executive Technology & Product Leader specializing in ADAS and software-defined vehicles

Cupertino, CA14y exp

“Automotive technology leader who has driven ADAS/autonomous driving (L2++ to L4) and AI-assistance roadmaps for Mercedes and Volkswagen Group. In current role, built a software-defined vehicle organization from a handful of people to ~150 supporting VW, Porsche, and Audi, while setting scalable architecture foundations across brands and vehicle classes.”

Kaushik Mukherjee

Screened

Executive technology leader specializing in search, ads, and data/AI platforms

Bangalore, India

“Engineering/technology leader with payments (UPI) scale experience who built fraud and growth systems for tens of millions of daily transactions while keeping CAC and cost guardrails. Scaled an engineering org from 12 to 155 in a year using platform + pod structures, and institutionalized canary deployments with auto-rollback (cutting degradations ~75%) while leveraging GenAI for code and test automation.”

Chinedu Enwere

Screened

Mid Software Engineer specializing in distributed backend systems

Redmond, WA4y exp

“Engineering candidate deeply embedded in AI-native development, currently using tools like Cursor and Claude Code to generate most of their code and building internal agents for on-call monitoring, anomaly detection, and automated incident mitigation. Particularly interesting for teams exploring AI-first engineering workflows, multi-agent development setups, and operational automation at scale.”

“Robotics student who built a fully autonomous "Pacman" robot car using ROS 2, integrating LiDAR/IMU sensing with localization, autonomous driving, and a custom RRT + A* planner. Demonstrated practical embedded optimization on an RP2040 by balancing replanning frequency with safety via rapid collision checks.”

“AI/LLM engineer with production experience at NVIDIA and Microsoft, including building a RAG-based enterprise knowledge assistant that improved accuracy by 42% and scaled to thousands of queries. Deep in inference optimization (TensorRT-LLM, Triton, quantization, speculative decoding) and MLOps/observability (Prometheus/Grafana, MLflow, LangSmith), plus orchestration with Kubeflow/Airflow across multi-cloud.”

“Operations/finance transformation leader with experience in large healthcare organizations (e.g., PacifiCare and KP), brought in to stabilize and modernize operating cadence during high-change periods. Known for applying lean/agile methods (value-stream mapping, 2-week sprints, kanban, RACI) to build KPI single-sources-of-truth and scalable analytics operating models, delivering 40%+ cycle-time/rework reductions and improved forecasting/decision velocity.”

“Operator focused on scaling and growing organizations, with experience in PE/VC-backed environments. Familiar with PE/VC and accelerator/studio models and how to apply them to create business agility and rapid experimentation while preserving innovation as companies scale.”

Yishi Wang

Screened

Junior Machine Learning & Data Science professional specializing in LLMs and analytics

Chicago, IL3y exp

“Amazon internship experience building production GenAI analytics for the returns organization: a multi-agent LLM+RAG system that let analysts query multiple heterogeneous data sources in natural language without hand-written SQL. Also built and operationalized four Apache Airflow DAGs for large-scale ETL, emphasizing observability and freshness-aware metadata to keep outputs accurate and up to date.”