Founding ML Research Engineer

About the Role

Join an early-stage industrial AI team building wearable AI assistants for field workers in sectors such as data center and energy infrastructure. You will own key parts of the multimodal AI stack, from production vision-language systems to edge inference and real-time voice and video experiences.

What You'll Do

• Build production agentic vision-language pipelines for wearable devices, including multi-step visual reasoning and tool use for industrial workflows.

• Develop evaluation systems, capture failure modes, and create data and fine-tuning loops that improve model quality over time.

• Adapt model orchestration and inference for changing connectivity, latency constraints, and edge hardware.

• Create real-time voice and video interfaces, including conversational and proactive assistance modes.

• Fine-tune open-source multimodal models for enterprise deployments, using techniques such as supervised fine-tuning, preference optimization, and quantization.

What We're Looking For

• Two to six years of experience shipping production vision-language or multimodal AI systems, with Python and PyTorch.

• End-to-end ownership of production model and orchestration systems, including visual reasoning, evaluation, and applied fine-tuning.

• Experience building multi-step agents with real tool use, evaluation harnesses, and data flywheels.

• A record of delivering AI products for end users and owning product outcomes, ideally through startup, founding-team, or greenfield product work.

• Strong CS, machine learning, or engineering fundamentals, typically demonstrated through a bachelor's or master's degree.

• Relevant experience with wearables, augmented reality, autonomous systems, edge inference, or on-premise model serving is beneficial.

• Familiarity with tools such as vLLM, Triton, Ray Serve, ONNX, TensorRT, Hugging Face Transformers, Docker, and model quantization is useful.

Compensation & Benefits

Base salary is $180,000 to $230,000 annually. Visa sponsorship is available.

Location

On-site, five days per week in San Francisco, California, United States.