Machine Learning Engineer

Harrison Clarke; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Harrison Clarke is seeking a technically talented engineer to join a well-funded AI startup in San Francisco, building cutting-edge ML systems at the intersection of data pipelines, model training, and production inference. You will own the full ML lifecycle—from data generation and training to deployment and monitoring, collaborating with researchers to turn ideas into reliable production systems.

A strong foundation in Python and ML frameworks, plus knowledge of transformers, is required, with

Qualifications

• Bachelor's or Master's in CS or a related technical discipline.
• Strong Python programming skills and experience with modern ML frameworks.
• Solid understanding of transformer architectures and large language models.
• Experience building production-quality ML systems.
• Comfortable working across both research and engineering environments.
• Strong software engineering fundamentals and systems thinking.

Responsibilities

• Building scalable data pipelines to collect, process, and generate large synthetic datasets for ML.
• Profiling and optimizing model training and inference performance.
• Deploying and maintaining high-throughput inference systems for LLMs.
• Working closely with researchers to translate new ideas into reliable production systems.
• Building tooling that supports the ML development lifecycle from experimentation through deployment.
• Contributing to technical research, experimentation, and engineering best practices.

Skills

Python programming

ML frameworks

Transformer architectures

Production ML systems

Software engineering fundamentals

Research & engineering collaboration

Education

Bachelors/Masters in CS or related

Tools

DeepSpeed

FSDP

Ray

Spark

Beam

Job description

We're working with a well-funded early-stage AI startup building cutting-edge machine learning systems at the intersection of large language models, distributed training, and production AI infrastructure.

This is an opportunity to join a highly technical team where engineers work across the full machine learning lifecycle, from large-scale data generation and model training through deployment, optimization, and production infrastructure. The team operates at the boundary of research and engineering, giving engineers the opportunity to contribute to new ideas while building systems that directly power real-world AI products.

What You'll Be Working On

• Building scalable data pipelines to collect, process, and generate large synthetic datasets for machine learning
• Profiling and optimizing model training and inference performance
• Deploying and maintaining high-throughput inference systems for large language models
• Working closely with researchers to translate new ideas into reliable production systems
• Building tooling that supports the complete machine learning development lifecycle, from experimentation through deployment and monitoring
• Contributing to technical research, experimentation, and engineering best practices

We're Looking For Someone Who Has

• Bachelor's or Master's degree in Computer Science or a related technical discipline
• Strong Python programming skills and experience with modern machine learning frameworks
• Solid understanding of transformer architectures and large language models
• Experience building production-quality machine learning systems
• Comfortable working across both research and engineering environments
• Strong software engineering fundamentals and systems thinking

Nice to Have

• Experience with GPU programming and performance optimization
• Familiarity with distributed training frameworks such as DeepSpeed, FSDP, Ray, or similar technologies
• Experience serving large language models using modern inference frameworks
• Experience building large-scale data processing pipelines using technologies such as Spark, Beam, or similar distributed systems
• Familiarity with workflow orchestration tools
• Experience with experiment tracking, MLOps, and production ML workflows
• Knowledge of cloud infrastructure and modern DevOps practices
• Experience designing scalable AI infrastructure supporting production machine learning workloads

Why Join

• Work on technically challenging problems at the intersection of AI research and production engineering
• Significant ownership across the full machine learning lifecycle
• Opportunity to influence architecture, infrastructure, and model development
• Collaborative environment where engineering and research work closely together
• Join a small, high-performing team building next-generation AI systems from the ground up