Lead Simulation & Autonomy Systems Architect

Grid Dynamics; Kraków; Presencial; PLN 240.000 - 360.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Medical insurance

Sports

Flexible schedule

Well-equipped office

Professional development

Corporate events

Descripción de la vacante

Grid Dynamics in Kraków seeks a Lead Simulation & Autonomy Systems Architect to own the simulation strategy for autonomy development and validation, and to lead the architecture for fault detection, safe state achievement, and degraded modes on customer-site machines.

You will code with AI agents, guide a small startup-style team, and contribute to the 2027 program charter, while collaborating with function leads and engineering teams across hardware and software.

Formación

• 10+ years of experience in autonomous systems, such as robotaxi, autonomous trucking, mining, agriculture or construction autonomy.
• Hands-on ownership of behavior, planning or control software in a deployed autonomy stack, including proven design of minimum-risk maneuvers, fallback trajectories and degraded modes.
• Integrating simulation into the development and validation loop, with scenarios, sensor models and data contracts between simulation and the autonomy stack (NVIDIA Isaac Sim, CARLA or equivalent).
• FMEA, hazard analysis and close work with functional safety teams (ISO 26262, ISO 21448 SOTIF or similar).
• Understanding of vehicle and powertrain dynamics, including braking and traction limits under load, on slopes and in rough terrain.
• Software architecture and hardware/software integration for real-time systems on embedded compute.
• Still writes code in C++ and Python, and is comfortable working through AI coding agents.

Responsabilidades

• Simulation Strategy: Own how simulation feeds autonomy development and validation, including scenario coverage, how real and simulated data are combined, and validation of degraded modes and safety scenarios in simulation.
• Failure Analysis & Safety Case: Lead FMEA and fault-tree analysis across perception, planning, control and hardware from day one, map each failure to a detection mechanism and a safe response, and work with safety engineering on a safety case covering acceptable event rates for the fleet and the signals that demonstrate due diligence.
• Degraded Modes & Reactive Safety: Design health monitors, run levels, fail-operational versus fail-safe behavior, minimum-risk conditions and fallback paths a simpler system can follow, plus fast reactive layers such as collision-imminent braking that doesn’t wait on the full perception pipeline while keeping false positives low.
• Cross-Cutting System Architecture: Define software/hardware integration, interfaces and system-level features beyond any single team, and bridge hardware and software timelines by telling hardware teams which requirements must be fixed now and which can wait.
• Hands-On Code with AI Agents: Write, review and guide code produced with AI coding agents inside guardrails such as automated tests and toolchain checks, and take degraded-mode architecture to implementation and testing on real machines.
• Roadmap & Team Leadership: Lead the simulation workstream’s contribution to the 2027 program charter and roadmap, guide two Simulation & Perception architects, and mentor engineers in a small, fast-moving, startup-style team.

Conocimientos

Autonomous systems

C++

Python

Simulation for Autonomy

Herramientas

NVIDIA Isaac Sim

CARLA

Descripción del empleo

We are seeking a Lead Simulation & Autonomy Systems Architect to join a physical AI program for a global heavy-equipment manufacturer with more than a decade of experience running autonomous machines in the field, as it takes a working autonomy stack toward a product that runs unsupervised at customer sites. In this role, you will work embedded in the client’s program with its function leads and engineering teams, owning the simulation strategy for autonomy development and validation, and leading the architecture for how the system detects faults, degrades gracefully and reaches a safe state, all backed by working code, not just diagrams.

Responsibilities

• Simulation Strategy: Own how simulation feeds autonomy development and validation, including scenario coverage, how real and simulated data are combined, and validation of degraded modes and safety scenarios in simulation.
• Failure Analysis & Safety Case: Lead FMEA and fault-tree analysis across perception, planning, control and hardware from day one, map each failure to a detection mechanism and a safe response, and work with safety engineering on a safety case covering acceptable event rates for the fleet and the signals that demonstrate due diligence.
• Degraded Modes & Reactive Safety: Design health monitors, run levels, fail-operational versus fail-safe behavior, minimum-risk conditions and fallback paths a simpler system can follow, plus fast reactive layers such as collision-imminent braking that doesn’t wait on the full perception pipeline while keeping false positives low.
• Cross-Cutting System Architecture: Define software/hardware integration, interfaces and system-level features beyond any single team, and bridge hardware and software timelines by telling hardware teams which requirements must be fixed now and which can wait.
• Hands-On Code with AI Agents: Write, review and guide code produced with AI coding agents inside guardrails such as automated tests and toolchain checks, and take degraded-mode architecture to implementation and testing on real machines.
• Roadmap & Team Leadership: Lead the simulation workstream’s contribution to the 2027 program charter and roadmap, guide two Simulation & Perception architects, and mentor engineers in a small, fast-moving, startup-style team.
  

Requirements

• 10+ years of experience in autonomous systems, such as robotaxi, autonomous trucking, mining, agriculture or construction autonomy.
• Behavior, Planning & Control: Hands-on ownership of behavior, planning or control software in a deployed autonomy stack, including proven design of minimum-risk maneuvers, fallback trajectories and degraded modes.
• Simulation for Autonomy: Integrating simulation into the development and validation loop, with scenarios, sensor models and data contracts between simulation and the autonomy stack (NVIDIA Isaac Sim, CARLA or equivalent).
• System Safety Practice: FMEA, hazard analysis and close work with functional safety teams (ISO 26262, ISO 21448 SOTIF or similar).
• Heavy-Machine Dynamics: Understanding of vehicle and powertrain dynamics, including braking and traction limits under load, on slopes and in rough terrain.
• Real-Time Systems Architecture: Software architecture and hardware/software integration for real-time systems on embedded compute.
• Hands-On Coding: Still writes code in C++ and Python, and is comfortable working through AI coding agents.
  

Nice to have

• Heavy-Duty & Off-Road Autonomy: Experience with autonomous trucks, mining haul or construction equipment, plus field and rapid-response engineering that turns field issues into fast fixes.
• Learned Driving Models: Experience with end-to-end learned driving or VLA models and wrapping them in safety envelopes.
• Synthetic Data: NVIDIA Omniverse and synthetic data generation for training and testing.
• Startup Mindset & Track Record: Doing more with less while managing several teams, and patents or publications in autonomy, safety or control.
• Travel Readiness: Availability for pre-approved trips to client sites.
  

We offer

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, sports
• Corporate social events
• Professional development opportunities
• Well-equipped office
  

About Us

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.