Staff ML Engineer

Who We Are:
For more than 25 years, people-driven companies have turned to Sequoia to get their employee experience right. We’re in this business because we know that taking great care of people leads to better business outcomes. Helping our clients achieve those outcomes is what drives our team, our strategic service offerings, and our technology forward.
Sequoia comes through for clients with guidance, service, and the Sequoia People Platform. Through their compensation, benefits, and overall people programs, we enable them to better manage their global workforce, reduce administrative burdens, and reach a deeper level of employee care and support. We strategically use technology to enhance the expert guidance and committed service we bring to every client engagement.
Role Overview:
As a Staff Machine Learning Engineer, you will design, build, deploy, and scale production-grade AI/ML and GenAI systems across Sequoia's People Platform. This is a hands-on technical leadership role for an engineer who excels at building intelligent systems from scratch, taking them to production, and continuously improving performance, reliability, cost, and business impact.
You will partner with Product, Engineering, Security, and business stakeholders to solve complex problems using AI while establishing scalable architectures, engineering best practices, and operational excellence.
What You'll Do:
1. Design, Build & Deliver -
Architect, develop, and deploy end-to-end ML and GenAI solutions, from ideation to production.
Design scalable ML systems, model serving infrastructure, RAG pipelines, agentic workflows, APIs, and intelligent applications.
Write high-quality production code and drive engineering best practices across testing, CI/CD, automation, and observability.
Make pragmatic architecture decisions balancing scalability, performance, reliability, security, and cost.
2. Productionize & Scale -
Own the full ML lifecycle, including data pipelines, model development, deployment, monitoring, evaluation, retraining, and governance.
Build enterprise-grade AI systems with strong focus on reliability, latency, resilience, observability, and user trust.
Establish MLOps and LLMOps practices for continuous delivery, experimentation, model versioning, performance measurement, and cost optimization.
Drive continuous improvement through monitoring, feedback loops, A/B testing, and production insights.
What You'll Bring -
14+ years of experience building large-scale software and AI/ML systems.
5+ years of experience deploying and operating ML solutions in production environments.
3+ years of hands-on experience building production GenAI applications.
Proven track record of taking AI/ML systems from concept to production with ownership of reliability, scalability, adoption, and business impact.
Strong software engineering and system design expertise, including distributed systems, cloud-native architectures, APIs, microservices, and platform engineering.
Excellent hands-on coding skills in Python and modern ML/AI frameworks.
Deep experience with MLOps/LLMOps, model serving, evaluation frameworks, monitoring, and production operations.
Experience with technologies such as LangGraph, LangChain, PyTorch, TensorFlow, Kubernetes, Docker, AWS/Azure/GCP, and observability platforms.
Sequoia’s Culture – Our most important asset
• Integrity
• Passion for service
• Innovative
• Growth oriented
• Caring for others
• Promise-centric
• Focused on Relationship Building
Sequoia provides equal opportunity to all applicants without regard to race, color, creed, religion, citizenship, national origin, age, sex, sexual orientation, gender identity, pregnancy, marital status, military or veteran status, disability, or any other basis prohibited by applicable law.
Compensation & Benefits
Sequoia provides competitive compensation including base salary, performance-based bonus programs, and comprehensive benefits package including 401(k) matching.
Sequoia’s Candidate Privacy Policy
https://www.sequoia.com/legal/candidate-privacy-policy

Work arrangement
No