Founding Software Engineer, Perception at Mountaineer Cleaning & Maintenance

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

15 Aug, 26

Salary

220000.0

Posted On

17 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Robot Policy Deployment, Model-Based Learning, Reinforcement Learning, Imitation Learning, Sim-to-Real, Multimodal Inputs, TensorRT, ONNX Runtime, TVM, CUDA Kernel Optimization, Perception Stack Development, Edge Hardware Optimization, Transformer Policies, Diffusion Policies, Classical Control

Industry

Description

Company Description At Kovari, we're rethinking how physical work gets done in the age of robotics. We believe building robots that can move the economy is one of the most important endeavors in technology. Our first goal is to build general-purpose robots for hospitality to take on physical, repetitive work that keeps the hospitality industry operating. The last mile problem for proliferating useful robots into businesses is a first class innovation problem itself. We aim to marry deep commercial understanding with fast paced innovation to create robots that move the industry. Since inception, we have raised over $6M to carry out our mission from industry leading investors. We are obsessed with rapid iteration, engineering rigor, and deploying real machines into real environments. The next decade will compress a century of progress in robotics, and we're looking for people who want to leave their fingerprints on that future. We are based in San Francisco and work in-person. The Role You will own Kovari's perception stack end-to-end—from raw sensor data to actionable representations for both learned policies and classical control. Your systems will run on deployed robots in real hotel environments, handling the messy realities of variable lighting, glass surfaces, temporary obstacles, and repetitive architecture. What You'll Do Research and develop high-reliability manipulation policies designed for high-velocity deployment and iteration Operate in a fast data flywheel across multiple data modalities Deep debug failure modes in transformer and diffusion policy field deployments Optimize policies for real-time (~10hz) inference on edge hardware What you bring Experience deploying robot policies on hardware No preference between model-based learning, reinforcement learning, or imitation learning Sim-to-real or real robot data Experience building policies with multimodal inputs (vision, depth, force/torque, proprioception) Experience with deep optimizations for constrained edge devices TensorRT, ONNX Runtime, or TVM for inference optimization CUDA kernel optimization Ideally, contributions at major robotics/ML conferences (CoRL, RSS, ICRA, NeurIPS) Values Pace of learning trumps everything else. Refining our craft is something we pursue relentlessly. Low ego, high ownership. Commitment to the mission. We work in-person, and this isn't a 9-to-5. We're building something hard, and we need people who are all-in.

Responsibilities

Own the end-to-end perception stack from raw sensor data to actionable representations for robots in hotel environments. Research and develop high-reliability manipulation policies and optimize them for real-time inference on edge hardware.