Founding Engineer - ML

About Datawizz

Datawizz is building the agent workforce. We're a seed-stage team backed by Human Capital (SpaceX, Snowflake, Anduril) building a platform that helps enterprises build and deploy agents — with the right permissions, guardrails, and activity logging to actually ship within companies. Early customers are already automating several hours of their workday.

We're hiring people who want to build, not manage. If you want to work on hard problems with a small team that ships, this is the place.

The Role

As a founding ML engineer, you'll build the core intelligence layer of our platform. You'll work on cutting-edge problems in agent orchestration, evaluation, and reliability - turning research ideas into production systems at scale. We strive to leverage and productize the latest research, while pushing the boundaries in specific areas where we have unique customer exposure with our own research. This role will include opportunities for publishable research alongside product work.

You will:

• Design and build our agent evaluation framework for measuring reliability, accuracy, and performance across diverse tasks.

• Develop and productionize the agent execution pipeline, including task decomposition, tool orchestration, and evaluation loops.

• Build and optimize our agent orchestration layer to route tasks, select tools, and manage multi-step execution.

• Contribute to infrastructure that enables rapid experimentation and agent iteration at scale.

• Influence technical direction and help shape the culture of the engineering team.

• This role is in-office, 5 days/week, based in San Francisco.

What We Are Looking For

We're looking for builders who are excited to push the boundaries of reliable, capable agents. You should be comfortable moving quickly, owning big pieces of the stack, and learning fast.

You might be a great fit if you have experience with:

• Training and evaluating ML models (especially LLMs) using Python, PyTorch, Transformers, TRL, Unsloth etc.

• Designing experiments and building metrics/evaluation pipelines

• Scaling ML systems from prototype to production

• Deploying and operating ML workloads in the cloud (AWS, Kubernetes, Docker, etc.)

• Thriving in fast-paced startup environments with high ownership

Benefits

• Competitive salary, based on experience level (Annual compensation range: $50,000-$500,000)

• Meaningful equity

• Opportunity to be a founding member of a growing company