Position Description
As a Senior AI/ML Engineer at Cinemo, you will play a key role in researching, developing, and bringing GenAI / LLM / NLP capabilities into production to elevate the in-car experience for intelligent cockpit systems. You will design and evolve both models and the underlying software architectures (e.g., AgenticAI frameworks) to ensure non-deterministic GenAI systems are safe, robust, and scalable—from cloud-based deployment to integration on automotive products running Android Automotive OS (AAOS) and Linux.
You will combine hands-on ML engineering with system-level thinking: building reliable agent workflows, automating evaluation and benchmarking, implementing guardrails, and continuously advancing the GenAI stack as the field evolves—bridging cloud services and embedded, in-vehicle constraints.
In this role, you will:
1. Lead GenAI / LLM / NLP research, development, tuning, and deployment for intelligent cockpit and infotainment experiences.
2. Design, implement, and operate production-grade AgenticAI frameworks (tool use, planning, memory, orchestration) within Cinemo’s middleware architecture.
3. Build and run cloud-based services and pipelines for scalable deployment, monitoring, and continuous improvement of GenAI systems.
4. Develop prompt engineering strategies and guardrailing mechanisms to meet automotive-grade requirements (safety, policy, latency, cost, quality).
5. Establish automated evaluation and benchmarking for non-deterministic systems (offline/online evaluation, regression testing, A/B testing).
6. Collaborate across teams to connect cloud GenAI capabilities with in-vehicle environments across diverse automotive hardware and OS platforms.
What you will need to succeed:
7. PhD or M.Sc. in Computer Science, Machine Learning/AI, Data Science, Computational Linguistics, Electrical Engineering, Robotics, or a closely related field.
8. Strong experience in NLP, Machine Learning, Deep Learning, and data preprocessing in production-oriented environments.
9. Proven expertise with Transformer architectures and modern LLM. ecosystems, including adaptation and evaluation for real-world applications.
10. Experience with AgenticAI concepts and agent frameworks (e.g., orchestration patterns, tool calling, retrieval, planning) and how to evaluate them.
11. Experience deploying and operating ML/GenAI systems in cloud-based infrastructures (e.g., containerization, CI/CD, monitoring, scalable services).
12. Proficiency in Python and common ML frameworks (PyTorch, TensorFlow, Keras) with strong written and spoken English communication skills.