Job description
NovaMind AI Labs is seeking a Senior AI Engineer to join our fast-growing team in San Francisco. We design and deploy state-of-the-art AI systems that power real-world products across industries. We value ruthless experimentation, pragmatic engineering, and cross-functional collaboration.
In this role, you will bring deep expertise in machine learning, data engineering, and scalable production systems to deliver reliable, low-latency AI features that scale with user demand.
What you'll get: impactful work, modern tooling, strong compensation, and a culture that values learning and ownership.
Responsibility
- Design and deploy scalable ML models and AI systems for production use, focusing on reliability and latency.
- Collaborate with product, data, and software teams to translate business problems into robust ML solutions.
- Develop end-to-end ML pipelines including data processing, feature stores, model training, evaluation, and deployment.
- Optimize models for performance, cost, and energy efficiency; implement monitoring and alerting for models in production.
- Lead research into new algorithms and techniques; contribute to internal docs, papers, or patents as appropriate.
- Mentor and code-review junior engineers; foster a culture of quality and shared ownership.
- Own MLOps practices: experiments tracking, versioning, CI/CD for AI, and reproducible training environments.
- Ensure data privacy, security, and compliance with regulatory requirements when handling sensitive data.
Qualification
- MS or PhD in Computer Science, Statistics, Mathematics, or a related field; 5+ years of industry experience in AI/ML.
- Strong programming skills in Python; hands-on experience with PyTorch or TensorFlow; familiarity with distributed computing.
- Experience with MLOps tools and pipelines (Docker, Kubernetes, MLflow, DVC, CI/CD).
- Proficiency with cloud platforms (AWS/GCP/Azure); building data pipelines and scalable services.
- Hands-on experience with NLP/LLMs or Computer Vision; active interest in deploying real-world AI.
- Sound understanding of model evaluation, experimentation design, and monitoring; strong debugging and problem-solving abilities.
- Excellent communication and collaboration skills; ability to work with cross-functional teams and present complex ideas clearly.
- Contributions to open source, publications, or patents are a plus.