Pre-screened and vetted in Arizona.
Senior Full-Stack Software Engineer specializing in FinTech, cloud microservices, and blockchain
“Python/ML engineer with strong DevOps depth: built an end-to-end regime-aware stock prediction system (custom fine-tuned FinBERT sentiment + technical/macro features) delivering a 12% accuracy lift. Also implemented Kubernetes/Helm + Jenkins/GitHub Actions pipelines (including GitOps-style workflows for multi-cloud Hyperledger Besu) and improved deployment speed/stability by ~50% while addressing race conditions and image drift.”
Mid-level Mechanical Design Engineer specializing in aerospace, automotive, and electro-mechanical hardware
Mid-level Robotics Software Engineer specializing in SLAM, perception, and UAV autonomy
Junior Software/Data Engineer specializing in data pipelines, dashboards, and full-stack web apps
“Backend engineer with research and industry experience building data-intensive systems for healthcare and IoT. Built Python/Flask/FastAPI services with real-time ingestion and ETL into relational databases, emphasizing data quality, performance tuning, and secure access controls (JWT, RBAC, row-level filtering). Notably caught hardware-driven sensor anomalies others missed and implemented quarantine/alerting to prevent bad data from corrupting analytics.”
Junior Mechanical Engineer specializing in energy storage and fuel cell systems
“Mechanical/manufacturing professional (not a software/Next.js candidate) who emphasizes applying Lean/Six Sigma-style manufacturing quality and continuous improvement tools (Kaizen, PFMEA/DFMEA, DOE, DMAIC, root cause analysis). Target base salary stated as 130,000.”
Junior Backend & ML Engineer specializing in distributed systems and MLOps
Junior Robotics Engineer specializing in SLAM, perception, and embedded motion capture
“Robotics software engineer with hands-on SLAM, ROS2, and distributed multi-robot systems experience. Improved MAST3R-SLAM loop-closure place recognition by changing the ASMK/ASMKS retrieval similarity metric (L2→L1) and validated on 9 TUM sequences, keeping near real-time performance despite a 25–30% retrieval cost increase. Also tuned MoveIt motion planning for a 6-DOF arm (12% higher maze completion rate) and built MQTT mesh communications for ESP32-based AMRs, using Gazebo+Docker and CI-style automation for reproducible testing and deployment.”