Vetted MATLAB Professionals

“Robotics software engineering intern at Bell Labs who overhauled indoor mobile robot localization in a ROS 2 stack, combining EKF + particle filtering with a neural network to handle BLE multipath disturbances. Delivered a major accuracy gain (~50 cm to sub-20 cm), earned a company Innovation award, published a paper, and saw the approach adopted across the company’s robot fleet.”

“Backend/data engineer focused on production ML/LLM systems: built a real-time FastAPI inference API on Kubernetes with strong reliability patterns (timeouts, idempotent retries, centralized error handling). Delivered AWS platforms using EKS + Lambda with GitHub Actions/Helm CI/CD and built Glue-based ETL from S3/Kafka into Snowflake with schema evolution and data-quality controls; also modernized legacy analytics/recommendation workflows into Python services with safe, feature-flagged cutovers.”

“Robotics software engineer with hands-on experience building a vision-guided grasping pipeline on a 7-DOF Franka arm, implementing gradient-based IK with null-space optimization and RRT* motion planning in ROS1. Strong in sim-to-real deployment and real-world debugging—addressed frame misalignment via hand-eye calibration and centralized TF configuration, and reduced replanning/jitter by tuning a weighted pose filter using rosbag replay and variance/grasp-time metrics. Also built an ESP32-based mobile robot architecture combining embedded decision-tree control with WiFi/web high-level commands.”

Ashi Sinha

Screened

Junior Software Engineer specializing in full-stack and ML/NLP systems

New York City, NY2y exp

“Entry-level full-stack engineer with internship experience at Amazon (Appstore IAP flow + uninstall recommendation workflow) and a health-tech startup (OneVector) where they built a DSUR reporting workflow end-to-end, including document generation, S3-backed versioning/metadata, and secure preview/download. Demonstrates strong production debugging and reliability mindset (instrumentation, deterministic retrieval, idempotent writes) and focuses on UX/performance in high-stakes user flows.”

Rakshak Shah

Screened

Intern Software Engineer specializing in cloud, AI, and systems programming

Seattle, WA1y exp

“AWS intern who significantly evolved a Drift Audit Service backend (Control Tower/EventBridge context) to make drift findings more explainable and reduce false positives by adding a verification step in Lambda before event ingestion. Demonstrates strong API design fundamentals in Python/FastAPI (contracts, idempotency, security controls) and careful rollout practices (feature flags, canaries, phased deployments).”

“Robotics software engineer who built and tuned a contact-implicit MPC controller for a full planar pushing manipulation pipeline (“Push Anything”), including a key fix for complementarity violations that eliminated “ghost pushes” and cut time-to-goal from 40s to 25s. Hands-on with ROS/MoveIt on real robot pick-and-place, improving hardware grasp reliability through TF/frame debugging, and uses Drake/URDF for simulation, contact detection, and MPC development.”

“Robotics software engineer/researcher with hands-on experience building a MuJoCo-based digital twin of a 6DOF soft-actuated manipulator, spanning robot design, custom actuator dynamics, classical control (PID/MPC), and RL (imitation learning and TD-MPC2 model-based RL). Also has ROS1-in-Docker SLAM integration/visualization experience and delivered a major trajectory-tracking improvement (error reduced from ~100mm to ~5mm) via Savgol smoothing, plus prototype fleet communications work for a solar-powered power line inspection robot.”

“Robotics engineer who worked on a lunar rover program, building a simulation environment that mirrored real hardware interfaces and incorporated moon-terrain slip/friction modeling validated against a physical “moon yard.” Also integrated an ML-based munition X-ray inspection system via REST APIs, deploying and scaling inference on Azure with Kubernetes plus Prometheus monitoring, load balancing, and self-healing reliability mechanisms.”

“Engineering leader with experience building point-of-care diagnostics platforms (IoT-connected PCR device delivering results in <15 minutes) and scaling multidisciplinary teams (55+). Has led major data/IoT architecture decisions (multi-cluster Kubernetes with secure routing; Kafka + Gobblin over MQTT) and runs execution with Agile roadmaps tightly aligned to GTM and senior leadership.”

“Embodied AI/robotics ML engineer with hands-on experience deploying POMDP-based reinforcement learning controllers on real mobile robots and vehicle fleets. Strong in sim-to-real robustness (domain randomization) and production rollout practices (HIL, shadow-mode, canaries, safety instrumentation), and has published related work (mentions a NeurIPS paper).”

Seongjae Ahn

Screened

Intern/Junior Robotics & Controls Engineer specializing in simulation, teleoperation, and diffusion policies

Berkeley, CA2y exp

“Robotics software engineer focused on simulation-to-teleoperation pipelines in NVIDIA Isaac Lab/Isaac Sim, including custom Dynamixel motor control integrated with USD/physics for dataset collection. Has hands-on ROS2 Humble + MoveIt2 integration for UR + Robotiq in Omniverse and builds Docker/CI workflows for GPU-enabled robotics stacks; also brings MPC coursework and multi-robot ocean drone comms experience (XBee/I2C).”

“Robotics engineer with UC Berkeley ROAR autonomous racing experience focused on real-time mapping/localization: implemented DLIO in ROS 2 and built the supporting LiDAR/IMU/GPS synchronization, TF consistency, and GPS-aligned trajectory tooling needed for reliable 3D SLAM on a physical vehicle. Also independently integrated a heterogeneous quadruped robot system at Eli Lilly spanning embedded, PLC, safety radar, Raspberry Pi, and cloud voice interfaces.”

YAKKALI PAVAN

Screened

Mid-level Machine Learning & Generative AI Engineer specializing in NLP, CV, and RAG systems

USA6y exp

“Built and deployed a production LLM-powered RAG document intelligence system used by non-technical enterprise stakeholders, cutting document search time by 40%+ while improving answer consistency. Demonstrates strong MLOps/data workflow orchestration (Airflow, AWS Step Functions, managed schedulers across GCP/Azure) and a metrics-driven approach to reliability, evaluation, and cost/latency optimization with guardrails and observability.”

“Robotics software engineer with extensive ROS2 academic project experience (UMDCP), including a drone-based 3D object reconstruction system using Mast3r where they built ROS2 nodes for autonomous image capture, containerized the ROS2/OpenCV stack for hardware deployment, and automated AWS uploads/compute-triggered reconstruction. Demonstrated strong sim-to-real debugging using ROS bags and PlotJuggler to correct yaw/trajectory offsets, and built multi-node TurtleBot navigation using visual cues (horizon/stop signal/obstacle detection) feeding a cmd_vel controller.”

“Data science/NLP practitioner with experience at NVIDIA and Microsoft building production-grade NLP and data-linking systems. Has delivered high-performing pipelines (e.g., F1 0.92) and large-scale entity resolution (F1 0.89), plus semantic search using embeddings and Pinecone with ~30–40% relevance gains, backed by rigorous validation (A/B tests, ROUGE, MRR) and strong MLOps/workflow tooling (Airflow, Databricks, FastAPI, MLflow, Prometheus/ELK).”