Machine Learning Engineer - Enterprise

About Boson AI

Boson AI is at the forefront of enterprise artificial intelligence innovation. The company focuses on advanced AI research, emphasizing large language models and autonomous agentic systems, aiming to resolve complex business challenges and generate significant value. The team comprises enthusiastic researchers and engineers dedicated to developing high-quality, dependable AI products that integrate seamlessly into enterprise workflows and establish new industry benchmarks.

Role Overview

The company is seeking a knowledgeable and meticulous Machine Learning Engineer to join its enterprise team. The engineer will lead the creation and deployment of innovative AI solutions, incorporating advanced models across language, voice, and vision domains. Responsibilities include mastering fine-tuning methodologies, constructing sophisticated AI workflows and platforms, and pioneering agentic AI systems capable of autonomous task execution while interfacing with diverse data and tools. The role demands a profound understanding of model behavior, a strong focus on detailed implementation, and dedication to reliability and quality in enterprise contexts.

Key Responsibilities

• Deliver end-to-end AI solutions addressing customer requirements by grasping user challenges, defining product specifications, and designing and building software powered by large language models.
• Benchmark models and develop evaluation metrics to identify and address model shortcomings as per customer needs.
• Create and deploy modern search technologies such as Retrieval-Augmented Generation (RAG) and DeepSearch to improve model grounding and leverage enterprise-specific information.
• Implement and refine fine-tuning approaches to tailor large models on domain-specialized data.
• Ensure models and agentic AI systems maintain high standards of quality, security, reliability, and scalability through careful execution and ongoing monitoring within demanding enterprise environments.
• Integrate diverse AI components into cohesive, scalable platforms.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, AI, or a related quantitative field, or equivalent practical expertise.
• Proven coding contributions available on GitHub; applicants should provide their GitHub profiles during application.
• Hands-on experience working with large language or multimodal AI models and their real-world applications.
• Background in building and implementing search systems.
• Strong ability to focus on detail while prioritizing product quality, reliability, and technical security.
• Competence in programming languages such as Python, Rust, TypeScript, or Go, alongside machine learning frameworks like PyTorch or JAX.
• Experience in designing and orchestrating multi-model workflows, particularly chaining large language models to automate multi-step tasks.

Preferred Skills

• Familiarity with developing or contributing to agentic AI products or platforms.
• Experience with cloud computing environments (AWS, GCP, Azure) and machine learning operations (MLOps) methodologies.
• Knowledge of distributed training and inference techniques for scalable AI model deployment.
• Expertise in system design, API creation, and infrastructure development for AI model management.
• Understanding of enterprise software integration approaches and data security principles.
• Strong grasp of HTTP and real-time communication protocols such as WebRTC, particularly relevant to voice AI applications.
• Excellent analytical and problem-solving abilities.
• Capacity to work independently and effectively drive projects forward within a dynamic, fast-paced setting.

Additional Information

Artificial intelligence tools may be utilized by the company to assist in parts of the hiring process, such as reviewing applications, resume analysis, and candidate response assessment. However, these AI tools augment rather than replace human decision-making. Final recruitment decisions remain the responsibility of human evaluators. For inquiries about data processing in the recruitment process, applicants are encouraged to reach out directly to the company.