Job Title

Position OverviewSenior-level data scientist role focused on building and deploying production NLP systems on bare metal infrastructure. This position requires a research-oriented mindset with the ability to build first-in-class products by translating cutting-edge research into innovative production solutions.Required QualificationsExperience- Minimum 5 years in data science/ML engineering roles - Minimum 3 years tenure in most recent organization in a relevant data science/ML role - Proven track record of deploying ML models to production - Experience managing bare metal server infrastructureTechnical SkillsSQL- Advanced query optimization and performance tuning - Complex joins, window functions, CTEs - Experience with Snowflake, BigQuery, or Redshift - Database performance analysis and indexing strategiesNLP Technology Stack- Transformer architectures - RAG pipeline implementation - LangChain, LlamaIndex, or similar frameworks - Vector databases: Pinecone, Weaviate, Chroma, FAISS - Model fine-tuning: LoRA, QLoRA - Embedding models and semantic search - Prompt engineering techniquesProgramming & ML Frameworks- Python (advanced level, production-grade code) - PyTorch or TensorFlow - HuggingFace Transformers - scikit-learn, XGBoost, LightGBMKey ResponsibilitiesTechnical Execution- Design and implement production NLP solutions using state-of-the-art language models - Build and optimize complex SQL data pipelines processing millions of records - Deploy ML models on bare metal GPU infrastructure - Configure and maintain GPU clusters for training and inference - Implement MLOps practices: versioning, monitoring, automated retraining - Optimize model inference for latency and throughput - Troubleshoot CUDA, driver, and hardware-level issues - Set up distributed training across physical servers - Research and prototype emerging ML techniquesLeadership & Strategy- Lead end-to-end ML projects from problem definition to production deployment - Drive innovation by researching and implementing first-in-class product features - Coordinate cross-functional teams including data engineers, domain experts, and full-stack developers to deliver integrated solutions - Define technical architecture and design decisions for ML systems - Drive adoption of ML best practices and engineering standards across teams - Collaborate with product and engineering leadership on ML roadmap and priorities - Present technical findings and recommendations to executive stakeholders - Own critical ML infrastructure decisions and vendor evaluations - Champion innovation by evaluating and integrating cutting-edge ML research - Lead cross-functional initiatives between data science, engineering, and product teams - Facilitate effective collaboration between technical and non-technical stakeholders - Translate latest research papers into production-ready solutions.Required Competencies- Research-oriented mindset with ability to innovate and build first-in-class products - Ability to work independently with minimal supervision and drive projects autonomously - Strong analytical and quantitative aptitude - Excellent problem-solving and logical reasoning skills - Proven ability to collaborate with cross-functional teams (data engineers, domain experts, full-stack developers) - Strong communication skills to translate technical concepts for non-technical stakeholders - Willingness to explore uncharted territory and experiment with novel approaches - Self-motivated with strong ownership mentality - Strong understanding of hardware constraints and optimization - Ability to work independently with bare metal infrastructure - Experience with both cloud and on-premise deployments - Proven ability to take projects from research to production - Track record of staying current with ML research and innovations - Strong debugging and troubleshooting skillsPreferred Qualifications- Experience with pre-training multi-modal models (vision-language, audio-text, etc.) - Hands-on experience with large-scale distributed training frameworks (DeepSpeed, FSDP, Megatron-LM) - Contributions to open source ML projects - Technical blog or active GitHub portfolio - Experience with model quantization and efficient inference - Publications or conference presentations - Knowledge of multi-modal architectures (CLIP, Flamingo, GPT-4V style models)