Sequoia
Website:
sequoia.com
Job details:
Staff Machine Learning Engineer
Role Overview
We are seeking a highly experienced Staff Machine Learning Engineer to lead the architecture, development, and scaling of enterprise-grade Machine Learning and Generative AI platforms.
As a senior technical leader, you will drive AI strategy, establish engineering best practices, mentor ML engineers, and collaborate cross-functionally with Product, Engineering, Data, and Business stakeholders to deliver measurable business outcomes. You will play a critical role in shaping Sequoia’s AI roadmap and building intelligent products that impact thousands of businesses globally.
The ideal candidate will have 12+ years of experience building and deploying large-scale ML systems, deep expertise across the full ML lifecycle, and hands-on experience delivering production-grade Generative AI solutions at scale.
Key Responsibilities
Technical Leadership
- Define and drive the technical vision for Machine Learning and Generative AI initiatives.
- Lead architecture reviews and establish best practices for scalable AI systems.
- Mentor and guide ML engineers and data scientists across teams.
- Influence product strategy through AI-driven innovation and technical thought leadership.
- Partner with Engineering leadership to build scalable, reliable, and secure AI platforms.
Machine Learning & Data Science
- Design, develop, and deploy large-scale ML solutions in production environments.
- Build advanced predictive models, recommendation systems, forecasting solutions, NLP applications, and deep learning systems.
- Drive the complete machine learning lifecycle:
- Problem definition
- Data acquisition and exploration
- Feature engineering
- Model development
- Model evaluation and validation
- Production deployment
- Monitoring, governance, and continuous improvement
- Develop frameworks and reusable components to accelerate ML development across teams.
- Establish model governance, explainability, fairness, and compliance standards.
Generative AI & LLM Applications
- Architect and deliver enterprise-scale GenAI solutions leveraging:
- OpenAI
- Azure OpenAI
- Anthropic Claude
- Llama
- Mistral
- Gemini
- Design and implement:
- Advanced RAG architectures
- Agentic AI systems
- Multi-agent workflows
- AI orchestration frameworks
- Prompt engineering and evaluation frameworks
- Fine-tuning and model adaptation pipelines
- Knowledge graph-assisted AI systems
- AI observability and evaluation frameworks
- Lead experimentation and adoption of emerging AI technologies to create competitive advantage.
Platform Engineering & MLOps
- Architect scalable ML platforms and infrastructure.
- Build and optimize end-to-end ML pipelines.
- Drive MLOps best practices including:
- CI/CD for ML
- Model serving
- Feature stores
- Experiment tracking
- Monitoring and observability
- Automated retraining pipelines
- Model governance and security
- Optimize system performance, scalability, reliability, and cost efficiency.
Cross-Functional Collaboration
- Partner with Product Managers, Engineering leaders, and Business stakeholders to identify high-impact AI opportunities.
- Translate business problems into scalable AI solutions.
- Define success metrics and measure business impact.
- Drive AI adoption and technical excellence across the organization.
Preferred Qualification Experience:
- 8+ years of experience in Machine Learning, Data Science, and AI Engineering.
- Proven track record of delivering production-grade AI/ML products at scale.
- Experience leading complex technical initiatives and influencing engineering direction.
- Experience mentoring engineers and driving technical excellence across teams.
Technical Skills:
- Strong expertise in Python, SQL, and distributed computing frameworks such as Spark.
- Deep knowledge of machine learning and deep learning frameworks:
- PyTorch
- TensorFlow
- Scikit-learn
- Strong expertise in:
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Agentic AI Systems
- Reinforcement Learning concepts
- AI Evaluation Frameworks
- Hands-on experience with:
- Docker
- Kubernetes
- AWS, Azure, or GCP
- Vector Databases
- API and Microservices Architecture
- Expertise in:
- MLOps
- Model Deployment
- Feature Stores
- Experiment Tracking
- Observability and Monitoring
Leadership Attributes
- Strong architectural and systems-thinking mindset.
- Ability to influence without authority and drive cross-functional alignment.
- Exceptional communication and stakeholder management skills.
- Passion for mentoring, innovation, and continuous learning.
Click on Apply to know more.
