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Atoms

Builds robots for food, mining and transport

Technical Lead - Autonomy Evaluation

Test Automation EngineeringConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA

About the Job

Requirements

  • A bachelor's degree in Computer Science, Robotics, Electrical Engineering, Statistics, or a related field, and 8+ years of relevant experience.
  • 2+ years as a technical lead or engineering manager for an evaluation, validation, or test team.
  • Ownership of an offline or system-level evaluation system for an autonomous vehicle or robotics program, or a comparable large-scale machine-learning model evaluation system in production, with accountability for its metrics and related release decisions.
  • Experience designing safety or behavior metrics for an autonomous system and defending them to engineering and executive audiences.
  • Hands-on experience building log replay at scale, including scoring against ground truth in open-loop and closed-loop evaluation.
  • Experience running evaluation pipelines on GPU clusters or comparable large-scale compute on a recurring cadence, with responsibility for reproducibility, throughput, and compute cost.
  • Experience with evaluation dataset design, benchmarking, and regression detection across software or model versions.
  • Working command of applied statistics for system validation, including statistical uncertainty, sampling, rare-event analysis, and the data volume a given claim requires.
  • Working understanding of autonomous systems end to end, including sensing, perception, localization, planning, and controls.
  • Experience on an autonomous vehicle or robotics platform.
  • Working experience with a robotics middleware and its logging and replay tooling.
  • Experience handling autonomous driving or robotics sensor data across multiple modalities and timestamps, including camera, LiDAR, and radar.
  • Proficiency in Python and the ability to read, navigate, and debug existing C++ codebases.

Responsibilities

  • Own the safety and behavior metrics a release is judged on, including collision and near-miss measures, trajectory agreement against ground truth, and comfort, as well as the go/no-go release criteria built on them.
  • Own the pipeline that converts recorded logs into scored test cases, covering open-loop and closed-loop evaluation of perception, localization, and planning against ground truth.
  • Own the system that benchmarks new software against old across the log corpus for every candidate release, including evaluation dataset design, regression detection, and triage of a regressed case to an owner.
  • Own how the corpus is mined for rare and important long-tail events, including sampling strategy, selection of logs by expected value per replay-hour, and coverage of scenario classes the corpus does not yet contain.
  • Own deterministic and reproducible evaluation execution, run identity, traceability of every result to a software version, and tracking cost per replay-hour.
  • Set practices for metric definitions, test design, code review, and writing evaluation results.
  • Mentor engineers joining the function, set the technical bar for their work, and participate in hiring.

About the Company

Atoms

Builds robots for food, mining and transport

Atoms builds machines and software that automate physical work in food, mining, and transport, and it makes specialized robots that each do one productive job rather than general-purpose humanoids. Its flagship business, CloudKitchens, builds delivery-only kitchens that restaurants rent to cook meals for delivery apps. At mines and quarries, its Pronto system adds sensors and driving software to existing haul trucks so they run on their own, while Otter software gathers restaurants' delivery-app orders in one place; Atoms earns money from kitchen rent, software subscriptions, and mining customers. The company was founded in 2016 and is based in Los Angeles, United States.

Company size
1,001-5,000
Company stage
Late Stage VC
Total funding
$1.7B
Headquarters
Downtown Los Angeles, CA
Founded
2016

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