Autonomy Software Engineer

Edison Smart®; Deutschland; On-site; EUR 70,000 - 100,000; Full time

Job summary

Edison Smart is seeking an Autonomy Software Engineer (Rust / Python) based in Germany to build the decision-making core of an autonomous platform. You will integrate reinforcement learning agents into high-performance runtime systems, enabling real-time perception, reasoning, and action in simulated, hardware-in-the-loop, and live testing environments.

You’ll work on pipelines from sensor data through RL inference to execution, connect Python RL agents to Rust runtimes for low-latency

Qualifications

• Backend or embedded development experience, preferably in Rust (Go or C++ also considered).
• Confidence working in Python on ML-heavy code.
• Clean, maintainable, high-performance code.
• Comfort with asynchronous working (PRs, RFCs, written discussion).
• A collaborative approach with code review and pair programming on complex hardware/software integration.
• Fluent English, written and spoken.
• Ethical considerations in engineering.

Responsibilities

• Build the decision-making core of an autonomous platform.
• Integrate RL agents into high-performance runtime systems.
• Create pipelines from sensor data through RL inference to execution.
• Connect Python RL agents to Rust runtime for low-latency inference.
• Develop distributed systems handling intermittent connectivity and bespoke hardware protocols.
• Set up training infrastructure, evaluation pipelines, and large-scale runs on simulators.
• Mentor colleagues and contribute to interview processes.
• Review code, RFCs, and proposals to uphold safety-critical standards.

Skills

Rust backend

Python ML

Go/C++ familiarity

Asynchronous work

English fluency

Collaborative coding

Job description

Autonomy Software Engineer (Rust / Python)

Must be based in Germany | English-speaking team | 12-month contract

About the company

Our client is a well-funded European deep-tech company that builds autonomous systems for demanding, real-world environments. Their engineering culture is built on reliability, collaboration and turning cutting-edge research into systems that actually operate in the field.

The opportunity

You’ll build the decision-making core of a new autonomous platform designed from the ground up. You’ll integrate reinforcement learning agents into high-performance runtime systems so the platform can perceive, reason and act on its own in real time. Your work will go from simulation through hardware-in-the-loop testing to live trials.

What you’ll work on

• Building reliable pipelines that run from sensor data through RL inference to execution, including handling edge cases and failure modes
• Connecting Python-based RL agents to Rust runtime systems for low-latency, reproducible inference
• Distributed systems and communications that cope with intermittent connectivity and bespoke hardware protocols
• Training infrastructure: distributed training, evaluation pipelines and large-scale runs on custom simulators
• Maintaining and improving a Rust and Python stack, cutting technical debt and raising reliability and performance
• Growing the test and benchmark suite, from unit level to HIL simulation and live testing
• Reviewing code, proposals and RFCs to keep standards high for safety-critical software
• Mentoring colleagues and helping shape the interview process

What you’ll bring

• Strong backend or embedded development experience, ideally in Rust (Go or C++ also considered)
• Confidence working in Python on ML-heavy code
• Clean, maintainable, high-performance code
• Comfort with asynchronous working (PRs, RFCs, written discussion)
• A collaborative approach, including code review and pair programming on complex hardware and software integration
• Fluent English, written and spoken
• A view that ethical considerations are part of engineering

Nice to have

• Rust and Python together in production
• Experience deploying RL or ML agents in real-world systems
• Safety-critical programming experience
• Familiarity with low-level control systems and their interfaces
• Sensor integration (IMU, GPS, lidar, cameras) and sensor fusion for navigation
• Robotics or control systems backgroundProduction ML systems
• Hardware interfacing (e.g. microcontrollers)

Why apply

• Work on a novel autonomous system that leaves the lab
• A mix of state-of-the-art research and real-world engineering
• A strong learning culture with tech talks, study groups and mentoring

The client actively encourages applications from under-represented groups, even if you don’t meet every requirement listed.