Vetted Research Assistants in New York

Manasa Pantra

Screened ReferencesStrong rec.

Junior Software Engineer specializing in AI, LLM systems, and full-stack development

Stony Brook, NY2y exp

“Product-focused full-stack engineer at startup (Zippy) who shipped a production multi-agent AI system for restaurant operations plus payments workflows. Built end-to-end: RAG grounded on a Notion knowledge base, structured function-calling task routing, FastAPI/JWT multi-tenant backend, and a polished React+TypeScript owner dashboard. Has real production incident experience (duplicate Stripe webhooks) and reports ~94% task-routing accuracy under load.”

Divyansh Agarwal

Screened

Junior Machine Learning Engineer specializing in computer vision and LLM applications

New York, NY3y exp

“Built and led an autonomous driving software effort for Formula Student, owning the full autonomy stack (perception, planning, control) orchestrated in ROS. Implemented stereo depth + YOLO object detection, RRT/RRT* planning, and a robust SLAM pipeline (Kalman filter, submapping) while leveraging Gazebo simulation and modern deployment tooling (Docker/Kubernetes, AWS, GitHub Actions CI/CD).”

“Backend/API engineer with ~4 years experience building production services in .NET Core/PostgreSQL/Redis/Docker and optimizing real-world latency issues (claims ~60% response-time improvement). Also built and owned an end-to-end RAG-based AI assistant using Python/FastAPI, OpenAI APIs, and Pinecone, plus agentic workflows with reliability guardrails (retries, confidence thresholds, monitoring). Currently pursuing a master’s degree and targeting a $150k base salary.”

“Master’s-level IoT course project that the candidate helped evolve into a research lab effort by “ROSifying” a soil-fertility detection rover (autonomous navigation within a GPS geofence, sensor fusion, and rover-to-base-station telemetry via NRF24 to a Raspberry Pi dashboard). Also built a ROS/Gazebo vision-based teleoperation system using a SigLIP hand-gesture model mapped to geometry_msgs/Twist, and improved stability by instrumenting and filtering a latency-prone perception-to-control pipeline.”

“Full-stack product engineer who has owned complex internal platforms end-to-end, spanning React/TypeScript frontends, Flask/Redis backend systems, and relational data design. Particularly strong at turning technically dense workflows into intuitive user experiences, including a synthetic-imagery platform adopted by multiple Army research labs and a marketing analytics system with 99.99%+ uptime.”

“Graduate student researcher on the EARTH project (college collaboration with Moog) working on robotics for an arm/bucket system. Implemented waypoint-based path planning, built an Apriltag data pipeline, and developed ROS 2 tooling including a joystick-to-DeltaCAN teleop node; exploring reinforcement learning policies trained from Tera simulator + ROS 2 bag data to optimize trajectory planning under varying pressure/load conditions.”

“Early-career engineer from BNP Paribas who drove a large-scale observability modernization—selecting and implementing Prometheus/Grafana for a 2000+ server estate, then productionizing it on Kubernetes via Docker/Jenkins. Known for hands-on demos, strong documentation/templates, and pragmatic troubleshooting (including custom Python metrics) that improved visibility and cut debugging time by ~60%.”

Ashutosh Khatavkar

Screened ReferencesStrong rec.

Mid-level SDET/Software Engineer specializing in test automation and CI/CD

Syracuse, NY3y exp

“AAA game QA professional from Ubisoft (For Honor) with deep live-service multiplayer experience. Known for owning network/competitive integrity risks and building a custom network simulation tool to reliably reproduce desync issues, accelerating debugging and saving 100+ hours. Strong end-to-end QA process skills spanning test planning, triage, regression, and release verification using JIRA/TestRail.”

Preetham Dandu

Screened ReferencesModerate rec.

Junior Software Engineer specializing in backend, distributed systems, and cloud platforms

Stony Brook, NY1y exp

“MS candidate with strong backend/data engineering focus who builds research and data systems with production-grade rigor (reproducibility, observability, restartability). Has hands-on experience securing and scaling FastAPI-based gateways in front of Java microservices, leading SQL Server→Snowflake migrations with dual-write/feature-flag rollouts, and hardening Kafka-based fleet-tracking systems against out-of-order and duplicate events.”

“Robotics/perception engineer focused on production-grade, real-time systems—optimized self-supervised segmentation on Jetson Nano from ~6–10 FPS to ~20–25 FPS and scaled experimentation/deployment by unifying 15+ edge models in a modular PyTorch Lightning framework. Experienced integrating distributed LiDAR-camera fusion via gRPC/protobuf into mission planning, migrating ROS1→ROS2 Foxy for multi-drone perception, and adding Prometheus-based observability for long-running deployments.”

“Full-stack AI engineer who has owned production deployments for both a voice journaling/emotional insights app and a RAG-based research assistant. Stands out for turning messy, failure-prone LLM and document pipelines into reliable user-facing systems through strong debugging, staged workflow design, and post-launch stabilization.”

Koushik Alapati

Screened

Mid-level Robotics Software Engineer specializing in perception, localization, and autonomous navigation

New York, USA4y exp

“Robotics software engineer with hands-on ROS2 experience building perception-driven navigation for AMRs, integrating YOLO11 + Depth Anything V2 and multi-sensor fusion (LiDAR/RGB-D/IMU) to boost pose accuracy by 30%. Strong in real-time debugging and edge deployment on NVIDIA Jetson (ONNX/CUDA), plus cloud-enabled telemetry (Azure) and simulation-driven testing (Isaac Sim) that cut physical test cycles by 25%.”

“Robotics/perception engineer who led and owned ARC, a cooperative perception system for autonomous vehicles that aligns and fuses multi-vehicle LiDAR point clouds in real time. Built a ROS-based multi-node pipeline with grid-based spatial reasoning and motion-compensated data sharing, achieving <20 ms compute latency and sub-7 cm alignment error; accepted to ACM SenSys 2026.”