Staff Machine Learning Engineer

Sonatus; Ireland; On-site; EUR 120,000 - 180,000; Full time

Benefits offered by this job

Stock option program

Flexible working arrangements

Unlimited PTO

Comprehensive benefits

Free snacks

Job summary

Sonatus is seeking a Staff Machine Learning Engineer to lead Edge AI efforts for in-vehicle health monitoring and prediction. You will own the ML pipeline from data ingestion to deployment on resource-constrained edge devices, optimizing models for embedded hardware and collaborating with a team of ML and vehicle software experts.

You will explore advanced architectures, leverage cloud-based APIs, and mentor junior engineers while shaping the next generation of self-aware vehicles.

Qualifications

• Bachelor’s degree or higher in CS/Engineering or related field.
• Strong background in machine learning with edge deployment experience.
• Experience with embedded systems and memory-constrained environments.

Responsibilities

• Lead the development of Edge AI for in-vehicle health monitoring and prediction.
• Own end-to-end ML pipeline from data ingestion to edge deployment and optimization.
• Collaborate with other developers and ML/vehicle integration teams on ML Ops and vehicle software.
• Experiment with cutting-edge model architectures and tools.
• Build and train Edge AI models (Transformers, CNN, LSTM) to process logs and sensor data.
• Port and optimize models for CPU/GPU/embedded NPUs; apply quantization, pruning, and distillation.

Skills

PyTorch/TensorFlow

Python

Edge AI

Embedded Systems

NVIDIA TensorRT

Linux/QNX debugging

CAN/UDS/Some/IP

Computer Vision

Anomaly Detection

ML Ops

ML Pipelines

NLP Techniques

C++ (Modern)

Education

MS/PhD in Computer Science or related field

Bachelor’s degree in CS/Engineering or related

Tools

ONNX

TFLite

TVM

scikit-learn

tslearn

statsmodels

Job description

• We are looking for a great Staff Machine Learning Engineer to join our seasoned ML team and lead the development of Edge AI for in-vehicle self-aware health monitoring and prediction
• In this role, you will build and deploy AI models that analyze continuous data generated in the vehicle during the day-to-day operation, including system logs, traces and vehicle internal signals (Ethernet and CAN) to detect and predict health of different sub-systems and anticipate failures in real-time
• You will own the end-to-end ML pipeline—from data ingestion and model training to deployment on resource-constrained edge devices and model optimization
• You will work in a fast-paced startup environment where your code will directly impact fleet reliability and build the next generation of the self-aware vehicle
• You will be expected to collaborate with other leading developers who have deep understanding and expertise of vehicle software and systems and other AI developers working on ML ops and integration of AI models on vehicles expected to be on the road today
• Expect to experiment with cutting-edge model architectures and best-in-class development tools
• Build and train AI Edge models (e.g., Transformers, LLM, CNN, LSTM) to process unstructured application logs, kernel traces and multi-modalities
• Integrating ML flows, including cloud-based LLM APIs (Gemini, OpenAI, Claude), with emphasis on synthetic data creation
• Develop algorithms to automatically cluster log patterns and detect software regressions, race conditions, or crash precursors
• Design unsupervised and supervised learning models (e.g., Autoencoders, Isolation Forests) to monitor time-series data from CAN bus and on-board sensors
• Implement logic to correlate signal anomalies (e.g., voltage spikes, latency jitters), across different modalities with system events to identify root causes
• Port and optimize pytorch/TensorFlow models into production-grade for execution on CPU/GPU bound targets or embedded NPUs
• Apply quantization, pruning, distillation and memory optimization to ensure models run within strict RAM/Flash budgets (think MBs, not GBs)
• Define the data strategy for on-device filtering: pre-processing on device and decide which data is processed locally versus processed in the cloud
• Lead the architecture for the edge ML pipeline and mentor junior engineers on best practices for embedded AI

Benefits

• Stock option program
• Flexible working arrangements
• Unlimited PTO
• Comprehensive benefits with generous allowance
• Free in-office food & snacks!

• Deep proficiency with PyTorch or TensorFlow, and experience with inference engines like ONNX, TFLite, or TVM
• Experience with libraries like scikit-learn, tslearn, or statsmodels for anomaly detection on sensor data
• Experience with NLP techniques for textual data parsing, sequence modeling (RNN/GRU), or lightweight LLMs/SLMs
• Expert Python (for training) and decent working knowledge of modern C++ (C++14/17 for inference)
• 7+ years in Machine Learning Engineering, with 3+ years focused on Edge AI or Embedded Systems
• Experience deploying to Edge environments (e.g. ARM based), managing memory manually, and working with limited compute resources
• Candidates with a strong Computer Vision (CV) track record are highly encouraged to apply
• Bachelor’s degree in Computer Science, Electrical Engineering, Software Engineering, or a related field
• Proven experience mentoring junior engineers in software development
• Proven ability to lead technical projects from concept to production in an ambiguous, fast-paced environment.
• Ability to communicate with stakeholders and articulate trade-offs
• Experience with NVIDIA TensorRT, Qualcomm SNPE
• Understanding of Linux/QNX kernel logs (dmesg), process states, and OS-level debugging
• Familiarity with Edge systems and preferably automotive (CAN (DBC files), UDS, SOME/IP, or MQTT)
• MS/PhD in Computer Science, Engineering, or related fields