Machine Learning Engineer

Why work at Operator?

• We care deeply about doing Great Work.

• We are solving a hard technology problem with an enormous market and impact opportunity on the other side.

• We are a small team of staff-level builders and intend to keep it this way. We hail from companies like Stripe, Coinbase, Figma, Primer and Pipe.

• You will get to be part of the founding team and build the company together with us. We mean it.

• We work in person out of our office in San Francisco.

Who we are looking for

We aim to build this company with ambitious, driven, and kind colleagues. These are the qualities we are looking for in our founding team:

• An extreme level of autonomy, ownership, and self-direction.

• Excellent written and verbal communication skills. We will ask for writing samples.

• Experience and/or a strong desire to work in an early-stage environment.

• Ability to take a long view of the world, but remain hyper focused on moving the needle every day.

• A demonstrated history of excellence in previous jobs, personal projects, or school.

What you will work on

• Experimenting with models to find the right balance between accuracy and speed.

• Building low latency applications for speech, computer use, language+reasoning models.

• Designing eval sets and exploring self-improving architectures.

• Building a sim environment for continuous end to end testing.

• Everyone on the founding team is expected to work extremely closely with our customers.

Must haves

• 3+ years of experience working on deep learning.

• 5+ years of professional experience.

• Strong Python programmer, and expertise in ML frameworks e.g. PyTorch, TensorFlow.

• Experience building, deploying and running ML infrastructure.

• Familiarity with the state of the art LLMs and their strengths/weaknesses.

• Experience doing 0-to-1 work on ML models and infrastructure.

• High level of autonomy and self-direction.

Nice to haves

• Experience training, fine-tuning and prompt engineering generative models and LLMs.

• Experience with vector databases, embedding models and built real-world RAG pipeline construction.

• Used and tuned ASR and TTS models.

• You have deeply thought about or tinkered with LAM models—agents that can reason and perform actions to accomplish tasks.

• Experience working at early-stage and fast-growing companies.