Greylock Partners

About the Company

We are the first partner for founders. Over 80% of our investments are the first check: Pre-Seed, Seed, or Series A. Many start on a whiteboard. Focused on AI-first companies. We partner selectively, care deeply, and strive for excellence.

We back founders who are building disruptive enterprise and consumer software companies such as: Airbnb (Nasdaq: ABNB), Abnormal Security, Adept AI, AppDynamics, Arista Networks (NYSE: ANET), CATO Networks, Coinbase (Nasdaq: COIN), Discord, Dropbox (Nasdaq: DBX), Figma, Inflection, Instabase, LinkedIn, Meta (Nasdaq: Meta), Nextdoor (NYSE: KIND), Okta (Nasdaq: OKTA), Palo Alto Networks (NYSE: PANW), Roblox (NYSE: RBLX), Rubrik, and Workday (NYSE: WDAY).

Listed Jobs

Company Name

Greylock Partners

Job Title

Machine Learning Infrastructure Engineers (Multiple Opportunities)

Job Description

Job Title: Machine Learning Infrastructure Engineer Role Summary: Design, build, and operate scalable, reliable infrastructure to support machine learning workloads across multiple startup investments. Expectations: 3+ years of industry experience in ML infrastructure or AI engineering; ability to collaborate with data science and product teams; strong background in distributed systems and cloud platforms. Key Responsibilities: • Architect and maintain production-grade ML pipelines (data ingestion, preprocessing, model training, inference). • Design and deploy scalable, fault‑tolerant compute and storage solutions (containers, Kubernetes, GPU clusters, object storage). • Automate CI/CD for ML workflows, including model versioning, testing, and rollout. • Monitor performance, resource utilization, and reliability; implement metrics, alerts, and tuning. • Collaborate with security, compliance, and operations teams to enforce best practices. • Optimize cost and performance for large‑scale ML workloads. Required Skills: • Proficiency in distributed systems concepts (networking, fault tolerance, load balancing). • Hands‑on experience with cloud providers (AWS, GCP, Azure) and container orchestration (Kubernetes). • Comfortable with infrastructure-as-code (Terraform, CloudFormation, Pulumi). • Scripting/automation skills (Python, Bash, Go). • Knowledge of ML frameworks (TensorFlow, PyTorch, MLflow) and data pipelines (Spark, Beam). • Familiarity with monitoring/observability tools (Prometheus, Grafana, ELK). • Strong debugging and troubleshooting abilities. Required Education & Certifications: • Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field. • Valid certifications such as AWS Certified Solutions Architect, GCP Professional Cloud Architect, or similar are a plus.

San francisco bay, United states

Hybrid

Junior

18-11-2025

Company Name

Greylock Partners

Job Title

Founding Machine Learning Engineer

Job Description

**Job Title** Founding Machine Learning Engineer **Role Summary** Design, develop, and own the full end‑to‑end machine learning lifecycle for a first‑of‑its‑kind cybersecurity solution. Convert research‑grade experiments into stable, reproducible, production‑ready models running on cloud platforms (GCP/AWS). **Expectations** - Full ownership of model development, training pipelines, and deployment. - Deliver repeatable, enterprise‑ready ML workflows that can scale across distributed resources. - Work cross‑functionally with data, infra, and security teams to translate research into product. **Key Responsibilities** 1. Architect and implement ML pipelines from scratch (data prep, distributed training, inference). 2. Design, train, and evaluate large‑scale LLMs on complex datasets. 3. Build experiment tracking, hyperparameter optimization, and metric dashboards. 4. Optimize compute usage and cost on cloud platforms (GCP/AWS). 5. Ensure reproducibility and robustness of models; prepare for deployment and monitoring. 6. Collaborate with security, product, and infra teams to integrate ML into the overall solution. **Required Skills** - Deep expertise in cloud ML services (GCP AI Platform, AWS SageMaker, or equivalent). - Proficiency in MLOps: CI/CD, model versioning, containerization, and orchestration (K8s, Airflow). - Experience training and deploying large‑scale LLMs in production. - Strong background in distributed training, data preprocessing, and experiment tracking tools (MLflow, SageMaker Pipelines, etc.). - Ability to conduct systematic hyperparameter tuning and performance evaluation. - Excellent cross‑functional communication and zero‑to‑one product development experience. **Required Education & Certifications** - Advanced degree (Master’s or Ph.D.) in Computer Science, Machine Learning, Data Science, or related field. - 4+ years of industry experience in ML engineering, with at least 1 year of LLM production experience. - Relevant cloud certifications (AWS Certified Machine Learning – Specialty, GCP Professional ML Engineer, etc.) preferred.

Redwood city, United states

Hybrid

Junior

18-11-2025