Job Description
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Company Overview
A leading enterprise technology organization develops AI-powered customer relationship management and workplace collaboration solutions. Its technology portfolio combines artificial intelligence, automation, cloud platforms, and collaboration tools to help organizations improve productivity, customer engagement, and business operations.
Key Responsibilities
- Design and execute fine-tuning strategies for large language models and other deep learning architectures tailored to NLP tasks such as summarization, ranking, classification, and generation.
- Own the complete model training lifecycle, including data curation, training infrastructure, hyperparameter optimization, evaluation, deployment, and monitoring.
- Build and maintain scalable fine-tuning pipelines running on GPU infrastructure.
- Optimize model architectures and training processes for production applications.
- Develop machine learning solutions that serve large-scale user populations with high reliability and performance.
- Collaborate with Product Managers, Designers, Frontend Engineers, and other specialists to conceptualize and develop new AI-powered product features.
- Lead or make significant technical contributions to large, cross-functional projects with substantial business impact.
- Mentor other engineers and provide detailed technical and code reviews.
- Improve engineering standards, tooling, development processes, and machine learning infrastructure.
- Measure and evaluate the effectiveness of complex ML and AI products using analytical and data-driven approaches.
- Lead technical architecture discussions and contribute to important technical decisions within the engineering team.
- Write understandable, testable, maintainable, and production-quality code.
- Take ownership of machine learning models from experimentation through production serving and ongoing monitoring.
Required Qualifications
- 5+ years of hands-on experience training and fine-tuning deep learning models in NLP or closely related fields such as speech, information retrieval, or multimodal machine learning.
- 5+ years of experience working with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- Demonstrated experience shipping fine-tuned machine learning models to production that serve real users at scale.
- Experience with functional or imperative programming languages such as PHP, Python, Ruby, Go, C, Scala, or Java.
- Strong analytical and data-driven mindset with the ability to define and measure success for complex ML and AI products.
- Experience leading technical architecture discussions and influencing technical decisions.
- Ability to write clear, understandable, testable, and maintainable production code.
- Strong communication skills and the ability to explain complex technical concepts to designers, support teams, and other technical and non-technical specialists.
- Strong understanding of machine learning model training, evaluation, deployment, and productionization.
- Experience developing reliable machine learning services for large-scale production environments.
Preferred Qualifications
- Expertise in recommendation systems or search technologies.
- Familiarity with model optimization techniques for inference, including quantization, pruning, speculative decoding, and model compilation.
- Experience with technologies such as TorchScript, TensorRT, or ONNX.
- Experience developing retrieval-augmented generation (RAG) and hybrid retrieval/generation systems.
- Broad experience across NLP, machine learning, and Generative AI.
- Experience working with structured, unstructured, and knowledge-graph data in RAG solutions.
- Understanding of large-scale model serving and inference optimization.
- Experience designing scalable machine learning data pipelines and experimentation systems.
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