Position Overview
Senior-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 Qualifications
Experience
- 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 infrastructure
Technical Skills
SQL
- Advanced query optimization and performance tuning
- Complex joins, window functions, CTEs
- Experience with Snowflake, BigQuery, or Redshift
- Database performance analysis and indexing strategies
NLP 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 techniques
Programming & ML Frameworks
- Python (advanced level, production-grade code)
- PyTorch or TensorFlow
- HuggingFace Transformers
- scikit-learn, XGBoost, LightGBM
Key Responsibilities
Technical 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 techniques
Leadership & 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 skills
Preferred 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)
