Deskripsi Pekerjaan
Cognitopia AI is at the forefront of artificial intelligence innovation, developing groundbreaking solutions that redefine industries. We're searching for an exceptional Senior Machine Learning Engineer to join our dynamic team in San Francisco. If you're passionate about pushing the boundaries of AI, thrive in a collaborative environment, and are eager to see your work deployed to impact millions, this is your opportunity.
As a Senior ML Engineer, you will be instrumental in designing, developing, and deploying advanced machine learning models across our core product suite. You will work with petabytes of data, cutting-edge research, and the latest cloud technologies to build intelligent systems that learn, adapt, and perform at scale. This role offers the chance to lead critical projects, mentor junior engineers, and directly contribute to our strategic vision.
Tanggung Jawab
- Lead the end-to-end development, training, and deployment of robust machine learning models for production environments.
- Architect scalable and efficient MLOps pipelines to automate model training, evaluation, and serving.
- Conduct rigorous experimentation and research to identify novel approaches and algorithms for complex problems.
- Collaborate closely with data scientists, software engineers, and product managers to define requirements and deliver high-impact solutions.
- Optimize existing models for performance, scalability, and cost-efficiency using advanced techniques.
- Provide technical leadership and mentorship to junior engineers, fostering a culture of continuous learning and excellence.
- Stay abreast of the latest advancements in AI/ML research and actively integrate relevant technologies.
Kualifikasi
- Master's or Ph.D. in Computer Science, Machine Learning, AI, or a related quantitative field.
- 5+ years of professional experience building and deploying machine learning models in a production setting.
- Deep expertise in Python and extensive experience with ML frameworks such as TensorFlow, PyTorch, or JAX.
- Proven track record with cloud platforms (AWS, GCP, or Azure) for ML infrastructure and services.
- Strong understanding of machine learning principles, algorithms, and statistical modeling.
- Experience with MLOps tools and practices (e.g., Docker, Kubernetes, MLflow, Kubeflow).
- Excellent problem-solving skills, with the ability to tackle complex, ambiguous challenges.
- Exceptional communication and collaboration skills, capable of working effectively in cross-functional teams.