---
title: 'Senior Machine Learning Engineer at Atoms'
canonical: 'https://feeny.ai/job/senior-machine-learning-engineer-atoms-san-francisco-tkabkggtj02k'
type: 'job'
last_seen: '2026-09-10'
---

# Senior Machine Learning Engineer at Atoms

- **Company:** Atoms
- **Location:** San Francisco, CA
- **Posted:** 2026-06-26
- **Last confirmed live:** 2026-09-10
- **Apply:** https://job-boards.greenhouse.io/cssmerge/jobs/8611100002

## Job description

## Who we are

Atoms is building the machines that power the next era of progress.

Over the last decade, software has transformed the digital world. But the physical world, where food is made, minerals are mined, goods are moved, and industries are run, remains far less intelligent, far less efficient, and far more constrained. We’re changing that.

Atoms builds Physical AI— real-world robots for the industries that move civilization forward, starting with food, mining, and transport. Our systems are designed to understand, predict, and control the real world with precision, turning complex physical operations into something more reliable, more scalable, and more productive.

This work requires more than robotics. It requires deep integration across hardware, software, AI, operations, manufacturing, and real estate. We don’t just build machines in a lab. We deploy them into real environments, operate them, learn from them, and improve them until they work at scale.

We are roboticists, engineers, operators, and builders. We believe the next great technology companies will not only transform information, but the physical systems that shape everyday life.

If you want to work on hard problems with real-world impact, join us.

AI Researcher (World Models & VLA)

## What you’ll do

A visionary Machine Learning Engineer to join our founding team who will help bridge the gap between high-level AI research and real-world physical actuation for our next-generation autonomous transport platforms. We are actively hiring across three core specialized subcategories: AI Research, Post-Training Optimization, and Data Engineering.

- Research and develop cutting edge RL and distillation techniques for trajectory planning
- Integrate emerging research from the broader AI community, identifying and prototyping the most promising solutions
- Design and deploy end-to-end multimodal models that translate real-time visual perception and high-level behavioral goals into physical vehicle actuation
- Develop interactive world models from raw multi-sensor logs, allowing the team to re-simulate events and query what a vehicle would see if it altered its trajectory
- Ensure core autonomous driving models can seamlessly adapt to novel urban environments and edge cases
- Partner with validation and QA teams to run model releases through rigorous simulated scenarios, detecting regressions and identifying systemic performance bottlenecks.

## What we’re looking for

- 4+ years of non-internship professional MLE experience.
- Deep expertise in applying AI Transformers to robotics, physical actuation, or spatial-temporal data.
- Proven track record designing or training multimodal systems, large-scale VLA models, or generative Diffusion models.
- Strong background in Sensor Fusion, combining inputs from Cameras, LiDAR, and Radar.
- Fluency in PyTorch or JAX for training large-scale models.
- Experience with multi-task learning, Birds-Eye-View (BEV) frameworks, representation learning, or data tokenization is highly preferred.
- Proficiency in Python and familiarity with C++.

Post-Training & Optimization

## What you’ll do

- Own the post-training lifecycle by distilling, quantizing, and optimizing massive models to run with low latency on vehicle edge hardware.
- Profile real-time inference pipelines to identify and eliminate CPU, GPU, and memory bandwidth bottlenecks on the vehicle.
- Work with low-level hardware, electrical, and firmware teams to iterate on custom carrier boards, sensor interfaces, and GPUs on edge devices.
- Benchmark and deploy models utilizing hardware-accelerated runtimes (e.g., TensorRT, CUDA) to minimize inference times under strict constraints.

## What we’re looking for

- 4+ years of non-internship professional MLE experience.
- Strong background in machine learning engineering with a focus on model optimization, distillation, and deployment.
- Hands-on experience optimizing models for edge deployment or custom embedded GPU targets.
- Deep understanding of profiling tools and debugging resource constraints across CPU/GPU boundaries.
- Experience with modern deep learning frameworks (PyTorch or JAX) and runtime compilation.
- Robust programming skills in Python and C++.
- Familiarity with low-level camera/sensor interfaces and robotics hardware is a significant plus.

Data & Long-Tail Scenarios

## What you’ll do

- Architect automated pipelines to ingest, filter, and identify rare, high-value, and long-tail scenarios out of multi-petabyte multi-sensor datasets.
- Target and extract complex structural corner cases from real-world driving logs to continuously feed, challenge, and improve our end-to-end behavior models.
- Iterate closely with QA, testing, and simulation teams to transform ambiguous real-world anomalies into concrete data blocks for simulation testing.
- Implement programmatic data curation, active learning strategies, and statistical quality metrics to optimize the signal-to-noise ratio of our training pipelines.

## What we’re looking for

- 4+ years of non-internship professional MLE experience.
- Professional experience building data curation pipelines, active learning workflows, or data mining architectures for massive physical datasets.
- Strong familiarity with robotics data structures and spatial frameworks, including Birds-Eye-View (BEV) or spatial tokenization.
- Experience processing and structuring raw data from Cameras, LiDAR, and Radar.
- Expert-level proficiency in Python, data engineering frameworks, and PyTorch/JAX.
- Exceptional ability to navigate, structure, and derive signal from highly ambiguous, messy, or undefined real-world data distributions.

## Why join us

At Atoms, you’ll work on one of the defining challenges of our time - bringing automation into the physical world to drive real, lasting impact. We exist to uncover valuable unknown truths and turn them into progress, which means constantly pushing beyond what’s known and building what doesn’t yet exist. The work is ambitious and often challenging, but it’s grounded in a shared sense of purpose and a team committed to seeing it through together. Our work only matters if it serves others, and we know that meaningful progress depends on the trust of the people we serve and the strength of our team—so we invest in both, creating an environment where you can do your best work and grow.

What else you need to know

This role is based in our San Francisco office. Atoms is a company driven by invention and continuous change - we are constantly reimagining our industries, building new products, and refining how we operate. We do our best work together. That’s why all of our office-based teams work onsite, five days a week.

The base salary range for this role is $208,000 - $263,500

Actual compensation will be determined on an individual basis and may vary depending on experience, skills, and qualifications.

Base salary is just one part of your total rewards package. You may also be eligible for equity awards.

Benefits Summary (USA Full-Time Exempt Employees):

- Medical, Dental, Vision, Disability, and Life Insurance
- Flexible Spending Account / Health Savings Account Options
- 401(k)
- Equity
- Sick Time, Unlimited Flexible Time Off, and Paid Holidays
- Paid Parental Leave
- Pre-Tax Commuter Benefit Plan
- Team lunch in our SoMa office every Tuesday and Thursday

Benefits are subject to change at the company's discretion. Atoms accepts applications on an ongoing basis.

Ready to join us as we serve those who serve others?

#LI-Onsite

## About Atoms

## Company Overview
- **One-liner**: Atoms builds physical automation systems—robotics, AI, software, and infrastructure—for mining, transport, and food industries to automate the physical world.
- **Entity Type**: Private (formerly City Storage Systems; rebranded from CloudKitchens/ProFood parent)
- **Headquarters**: Los Angeles, California, United States
- **Founded**: Rebranded as Atoms in March 2026; predecessor City Storage Systems founded earlier (~2018)
- **Founders**: Travis Kalanick (CEO)

## Core Business
- Primary industry: Physical AI, Robotics, Automation Machinery Manufacturing, Industrial Infrastructure
- Target customers: Mining/quarry operators, transportation & logistics companies, food service brands and kitchens (B2B, heavy industrial)
- Mission or purpose statement: “We build progress machines.” / “The constraint on human progress is our ability to automate the physical world.”

## Products & Services
- **Atoms Mining**: Autonomous haulage systems for mines and quarries designed to improve productivity, safety, and efficiency in complex industrial environments.
- **Atoms Transport**: Wheelbase for robots — autonomous delivery and logistics systems for moving goods. Includes the acquired Pronto AI technology (acquisition completed April 2026).
- **Atoms Food (ProFood)**: Robotics for better food. Automated kitchen systems that deliver consistent, affordable meals at scale; kitchen infrastructure platforms (bringing orders, delivery, operations into one platform); food real estate; Lab37 (an operating system for food facilities); and a forthcoming “built” meals brand.
- **Lab37**: The operating system/software layer that connects and orchestrates Atoms’ physical automation across domains.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Key Metric**: Total Funding – Not publicly disclosed; previously raised significant capital as City Storage Systems/CloudKitchens (estimated at over $1B in prior rounds); 414 employees on LinkedIn as of mid-2026
- **Notable Investors/Partners**: Travis Kalanick (founder/CEO, previously co-founded Uber); partners include mining/quarry operators, food brands (specific names not public)
- **Growth Signals**: 414 employees (+13.2% monthly growth); operates in 24 countries; 26 active job postings (+160% monthly); recent acquisition of Pronto AI (April 2026); rebranding from City Storage Systems to Atoms (March 2026) signals strategic consolidation and pivot; hiring across robotics, AI, software, hardware, operations globally

## Competitive Advantages
- **Cross-stack vertical integration**: Controls sensors, compute, software, manufacturing, operations, real estate, energy, and infrastructure — a rare “full-stack” approach to physical AI.
- **Founder advantage**: Led by Travis Kalanick (Uber co-founder) who has deep experience scaling category-defining companies and attracting top engineering/operations talent.
- **Real-world deployment**: Robots with “real jobs” — not lab demos — deployed in working mines, kitchens, and transport systems today.
- **Engineering culture**: Emphasis on “cross-stack competence,” ownership, and solving problems “most companies won’t attempt.”
- **Global footprint**: Presence in 24 countries allows rapid deployment and operations at scale.

## Strategic Focus
- Consolidating formerly separate brands (CloudKitchens, ProFood, Pronto AI) under one unified “Atoms” identity to create a single physical AI platform
- Expanding autonomous mining haulage into more quarries and mines globally
- Scaling food automation from kitchen robotics to end-to-end food infrastructure (real estate, ordering, delivery, operations)
- Growing AI, ML, and autonomy capabilities (hiring Senior Software Engineer - ML Infrastructure, Backend Infrastructure, Robotics Engineer)
- International expansion with dedicated regional roles (APAC, LATAM, Europe)

## Why Work Here
- **High ownership**: “Take ownership early, and contribute from day one” — interns and new hires work on live products, not side projects.
- **Hard problems**: Solving physical-world automation challenges that most companies won’t attempt — mining, logistics, food production at scale.
- **Compensation**: “Outsized rewards for outsized performance” — competitive compensation, healthcare, benefits, and PTO.
- **Culture**: “Builders” and “hardcore engineers” who have scaled category-defining companies — a relentless, high-expectation environment focused on excellence.
- **Work policy**: Mix of in-office roles (Mountain View, CA; Los Angeles, CA; New York, NY; Austin, TX) and international locations (Vilnius, LTU; Makati, PH; Bogotá, COL; São Paulo, BRA). Remote/hybrid policy not explicitly stated but many roles list specific city locations.
- **Tech stack**: Modern — Python, TypeScript, Go, Java, Kubernetes, Docker, PostgreSQL, Redis, GraphQL, gRPC, Bazel, Google Cloud, NVIDIA, dbt, Looker, Airflow.
- **Team composition**: 25% of employees in technical roles (154 people); strong presence of HR, legal, finance, and operations supporting global scale; talent sourced from CloudKitchens, Amazon, Uber, Google, ProFood Properties, City Storage Systems.

## Sources
1. [atoms.co](https://atoms.co/)
2. [atoms.co/careers](https://atoms.co/careers)
3. [atoms.co/openroles](https://atoms.co/openroles)
4. [LinkedIn – Atoms](https://www.linkedin.com/company/atomsco)
5. [Greenhouse – Atoms Jobs](http://job-boards.greenhouse.io/cssmerge)

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