ML Engineer / Applied Research Scientist
We seek an AI/ML specialist with a blend of engineering and applied research expertise to develop a foundational AI system for logical reasoning. Ideal candidates will have extensive experience applying a range of neural network architectures (e.g., natural language, vision, graph, symbolic, and formal domains) to diverse problems, using training and optimization techniques such as value models, reinforcement learning, and search strategies across multiple data types (text, audio, video). A strong background in graph theory, GNNs, and ML-based code synthesis or validation is highly valuable. We are especially interested in candidates who have applied ML in real-world engineering applications or who have consulted across varied industry domains.
Key Responsibilities
- Design, develop, deploy, and evaluate novel machine learning models and algorithms to establish theorem proving as a core framework for logical reasoning in AI.
- Utilize ML techniques, including supervised, unsupervised, and reinforcement learning.
- Preprocess, clean, and transform large datasets for model training and evaluation.
- Build data pipelines and ETL processes to ensure efficient data flow and accessibility.
- Design and execute experiments, analyze results, develop metrics and benchmarks, and optimize model performance.
Desired Experience & Qualifications:
- Master's or Ph.D. in Computer Science, Engineering, Mathematics, or a related field.
- 5+ years in machine learning, AI, or related fields.
- Proven experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
- Skilled with data engineering platforms and tools like Apache Spark or Hadoop.
- Experience with MLOps practices and tools for model deployment, monitoring, and logging (e.g., Prometheus, Grafana, ELK stack).
- Expertise in deep learning architectures across multiple domains (e.g., vision, NLP, graphs) with bonus knowledge in other NN architectures, such as symbolic or GraphML.
- Background in program/code synthesis or models for system design generation.
- Experience in applying ML to diverse engineering and real-world applications.
- Familiarity with cloud platforms and ML toolkits, containerization (Docker), and orchestration tools (Kubernetes).
- Familiarity with CI/CD tools such as Jenkins, GitLab CI, or CircleCI.
- Contributions to open-source projects within machine learning or AI communities.
