Vetted Reinforcement Learning Professionals

“Embodied AI/robotics-focused ML engineer who built a real-time assistive Braille device by coupling transformer OCR with an Arduino-controlled electromechanical Braille cell, solving tight latency and hardware-integration constraints. Has recent work on geometry-grounded world models and a real-time 4D reconstruction foundation model (Any4D), and delivered measurable impact at Voxel AI by building a scalable headless simulation + synthetic data pipeline that improved a safety-critical algorithm’s recall by ~16%.”

“Built and shipped an end-to-end LLM agent during an AT&T internship to automate network troubleshooting, with production-style reliability safeguards (timeouts/retries/fallbacks) and structured, state-machine orchestration; project won 3rd place in AT&T’s nationwide intern innovation challenge and was demoed to leadership. Also handled messy multi-partner data at Tencent by implementing schema validation/normalization, confidence-threshold fallbacks, and idempotent Python/ORM-based pipelines.”

Priyanka Kaswan

Screened

Senior AI Research Engineer specializing in LLM agents and large-scale ML

7y exp

“AT&T Labs builder who deployed a production multi-agent LLM system that lets engineers ask natural-language questions and automatically generates deterministic, schema-grounded Snowflake SQL (200–400 lines) to detect anomalies in massive wireless/network event data (~11B events/day). Experienced with LangChain and Palantir Foundry orchestration, RAG-based result interpretation, and rigorous evaluation/monitoring loops to continuously improve reliability.”

Ravikanth Kasamsetty

Screened

Executive AI/ML Engineering Leader specializing in cloud-native SaaS and GenAI platforms

23y exp

“Engineering leader who modernized and unified a fragmented product suite at Milestone via a multi-year cloud-native roadmap, delivering an MVP in three quarters and boosting team velocity by 40% through cross-functional squads. At Prometheum, led a trust-building hybrid architecture (AWS control plane + customer-hosted data plane) using Kubernetes to ensure sensitive enterprise data never left customer networks while remaining cloud-agnostic across providers.”

Dhaya Lakshminarayanan

Screened

Executive Operations Leader specializing in AI, climate tech, and high-growth organizations

San Francisco, CA20y exp

“Operator who has repeatedly stepped into founder-level, cross-functional ownership at startups—running full HR (recruiting, onboarding, benefits) with AI-driven process automation, and later serving as interim VP People & Culture during an executive transition. Also managed cap table, 409A, equity grants, and day-to-day investor relations, and built company-wide ELT goals/KPIs and operating rhythms tied to board-approved strategy.”

“Built and shipped a production LLM-powered medical scribe that generates structured clinical visit summaries using RAG, strict JSON schemas, and post-generation validation to reduce hallucinations. Experienced in making LLM workflows deterministic and observable (structured logging/metrics/tracing) and in evaluation-driven iteration with metrics like schema pass rate and edit rate; collaborated closely with clinicians and policy stakeholders at Scale AI to drive adoption.”

“Robotics engineer who helped bootstrap Meta’s humanoid robotics effort, building simulation training and deployment infrastructure for vision-language-action (VLA) models. Evaluated multiple physics backends (Bullet, MuJoCo, Isaac, internal) to minimize sim-to-real gap and addressed control-loop frequency mismatches via sequence optimization/MPC-like approaches and trajectory-output modifications. Published research that contributed a new addition to ROS 2 and has built ROS2 node stacks spanning control, perception, teleop, tactile sensing, and imaging.”