.png){kind=logo}
Vetted Deep Learning Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in Bayesian inference and reinforcement learning
Senior Software Engineer specializing in distributed systems, ML infrastructure, and search
Mid-level Agentic AI & ML Engineer specializing in LLM agents and RAG systems
Mid-level Data Scientist specializing in GenAI, LLMs, and MLOps
Mid-level Full-Stack Software Engineer specializing in cloud-native and data platforms
Senior AI & Systems Architect specializing in ML infrastructure and FinTech
Varmin Singh
Intern Software Engineer specializing in data engineering and LLM/RAG systems
“Built and productionized enterprise LLM/RAG systems, including a Boeing internal solution that gave 400+ program managers conversational access to 1M+ rows of schedule data, with strong emphasis on governance, reliability, and reducing hallucinations in tabular domains. Also has experience running developer-focused workshops (UC Berkeley computer architecture) and partnering with customer-facing stakeholders to drive adoption of a compliance-sensitive NLP product (SEC-aligned) at Penserra.”
Elizabeth Xu
Entry-Level Software Engineer specializing in ML/NLP and security
“Early-career engineer (internship background) who built a production-style notes product using Next.js App Router with Server Components/Server Actions and a Postgres-backed analytics model. Demonstrates strong performance and reliability instincts—measured DB latency improvements via indexing and cursor pagination, plus durable orchestration with Temporal using idempotency and deterministic workflows.”
Jacqueline Zhang
Mid-level Machine Learning Engineer specializing in LLMs, fairness, and healthcare ML
“ML/NLP practitioner with a master’s thesis focused on domain-adaptive knowledge distillation for LLMs (LLaMA2/sheared LLaMA), showing improved perplexity and ROUGE-L on biomedical data. Also built real-world data linking and search systems: integrated ClinicalTrials.gov with FAERS using fuzzy matching + embeddings, and delivered an LLM-powered FAQ recommender at Hyperledger using sentence-transformers, FAISS, and fine-tuning to mitigate embedding drift.”
Likhitha Bethi
Mid-level Software Engineer specializing in backend systems, distributed systems, and applied AI
“Goldman Sachs engineer who owned end-to-end features for an internal onboarding and case management platform, spanning React/TypeScript UI, a GraphQL gateway, and Node + Spring WebFlux microservices. Built and operated a Kafka-based ingestion and search pipeline with DLQs, retries, idempotency, and strong observability, and improved developer experience via backward-compatible GraphQL API design and schema-driven documentation.”
Param Yanamandra
Staff/Lead Software Architect specializing in Contact Center platforms and GenAI automation
“Built and deployed production LLM systems in healthcare and at LinkedIn: automated pen-and-paper clinical trial evaluations with a 40x efficiency gain and created an evidence-based Evaluation Agent focused on accuracy and speed. Also used Temporal to orchestrate resilient data-ingestion workflows for customer support staffing prediction, improving prediction outcomes by 40% while handling missing data, retries, and backfills.”
Saikrishna Swaminathan
Entry-level Aerospace/ADCS Researcher specializing in spacecraft controls and simulation
“Robotics/control-focused candidate with hands-on ROS2 + Gazebo experience implementing MPC with online state identification on a Crazyflie drone, including camera-based position determination fixes. Also worked on multi-agent spacecraft formation control and constellation optimization, debugging numerical drift and redesigning leader-follower control laws to handle delayed/outdated updates; uses Docker to ensure reproducible simulation results across machines.”
Chinmayee Wamorkar
Mid-level Robotics & Autonomy Engineer specializing in MPC, RL, and GPU-accelerated optimization
“Robotics software engineer from Ati Motors who brought a Linear MPC approach (based on Kuhne et al.) into production, rebuilding parts of the planning stack to eliminate oscillations and safely double AMR speed from 0.8 m/s to 1.6 m/s. Also delivered an end-to-end point-cloud detection pipeline (PointPillars) including synthetic data generation in Isaac Sim and TensorRT deployment for real-time human/trolley detection, with a strong focus on production reliability via iterative hardening and nightly SIL.”
Yue Yang
Intern Data Scientist specializing in GenAI (LLMs, RAG) and ML model optimization
“Built and deployed a production LLM-powered risk assistant for KPMG and Freddie Mac that lets analysts query a confidential Neo4j risk graph in natural language (no Cypher), turning multi-day analysis into minutes with traceable, cited answers. Implemented rigorous guardrails, deterministic verification, RBAC/security controls, and a full eval/observability stack, cutting query error rate by ~50% and iterating through weekly UAT with non-technical risk analysts.”
Derek Tuggle
Executive Robotics & Machine Learning Engineer specializing in industrial IoT controls
“VP of New Product Development at Axiom Cloud who built and scaled a "Virtual Battery" product that used supermarket frozen inventory as thermal energy storage—personally prototyped core control/safety logic in Python and led the engineering buildout through deployment and operations. Combines real-world industrial controls and edge deployment experience (LonWorks/Modbus, Docker/CI/CD) with an MS in CS focused on robotics, perception, and ML, including ROS 2 and YOLO-based perception.”
Siddhik Reddy Kurapati
Junior Controls & Motion Planning Engineer specializing in MPC, RL, and autonomous systems
“Robotics researcher focused on learning-based navigation: builds sub-goal generation and cost-to-go models (Bayesian network-based) integrated with motion planning and MPC/NMPC control. Has hands-on ROS 2 package development across vehicles, drones, and manipulators, and uses a broad simulation stack (Isaac Sim, Gazebo, MuJoCo, PyBullet, PX4) to test and integrate systems.”
Gurnoor Kaur
Intern Robotics Software Engineer specializing in motion planning and robot perception
“Robotics software engineer with Amazon Robotics internship experience who built a visual-servoing architecture from scratch, navigating multiple simulator pivots to achieve a closed-loop motion-planning and execution prototype. Currently working with ROS 2 on a medical assistive feeding robot using the Kinova Kortex platform (MoveIt2, ros2_control, Gazebo/RViz), and has demonstrated strong real-time debugging and distributed-system synchronization using Carbon and Docker.”
Meredith Ma
Entry-level AI/ML Software Engineer specializing in generative AI and computer vision
“Built and owned a production RAG coding assistant at Magna International used by 200 engineers, with hands-on work across React/TypeScript, retrieval infrastructure, and Postgres observability. Also brings an unusual blend of product UX thinking from AR game onboarding work, showing strength in both technical systems reliability and user activation.”
KaMing Cheung
Junior Software Engineer specializing in full-stack and machine learning
“CMU IoT coursework project builder who implemented an end-to-end TinyML gesture recognition system on a Particle Photon + ADXL345, streaming data via MQTT/Node-RED to a real-time Node.js frontend and deploying a quantized logistic regression model on-device. Also explored multi-drone coordination, implementing leader-follower offset control and a pivot/arc turning strategy to avoid collisions, and brings practical Docker/Kubernetes plus CI/CD workflow experience from internships.”
Kella Dhanush Venkata Sai
Junior ML Engineer specializing in Generative AI and LLM applications
“Built a production internal knowledge assistant using a RAG pipeline over large spreadsheets, PDFs, and support documents, using transformer embeddings stored in FAISS. Focused on real-world production challenges—format normalization, retrieval quality, hallucination reduction (context-only + citations), and latency—using hybrid retrieval, quantization, and containerized deployment, and communicated the workflow to non-technical stakeholders using simple analogies.”
ZHAOYANG ZHANG
Entry-level Financial Data Analyst specializing in analytics and compliance reporting
“Current financial data analyst at Maximus with hands-on experience building SQL and Python reporting pipelines for expenditure, refund, and retention analysis. Stands out for turning messy multi-source financial data into trusted dashboards, automating reporting to save 15+ hours weekly, and driving measurable reductions in refund rates through cohort-based analysis.”