---
title: 'Machine Learning Engineer – Motion Planning & Prediction at Avride'
canonical: 'https://feeny.ai/job/machine-learning-engineer-motion-planning-prediction-avride-austin-b2d5yy1aza2k'
type: 'job'
last_seen: '2026-09-11'
---

# Machine Learning Engineer – Motion Planning & Prediction at Avride

- **Company:** Avride
- **Location:** Austin, TX
- **Posted:** 2025-08-20
- **Last confirmed live:** 2026-09-11
- **Apply:** https://job-boards.greenhouse.io/avride/jobs/4013016009

## Job description

## About the team

Our team develops the core software and data processing systems that power motion planning and decision-making in autonomous vehicles. We work at the intersection of machine learning, large-scale data infrastructure, and real-time vehicle control, collaborating across engineering, analytics, and product teams to deliver safe and intelligent driving capabilities.

Before you apply: This role requires hands-on experience building systems that predict how other agents will move and deciding how a vehicle or robot should act in response — deployed on real hardware, not only in simulation or research. If your machine learning experience is primarily in NLP, recommendations, tabular data, or academic research without deployed systems, this specific role likely isn't the right fit, though we encourage you to look at our other openings

## About the role

We are looking for a creative & driven Machine Learning Engineer to join our autonomous vehicle team. You will be at the center of our efforts to build intelligent systems that can understand, predict, and safely navigate a complex and dynamic world. This role involves designing and training the next generation of deep learning models that form the brain of our vehicle, learning from petabytes of real-world driving data. If you are passionate about applying cutting-edge ML to solve high-stakes robotics challenges, we want to hear from you.

## About the Team

We build the software that decides how our autonomous vehicles move through the world. Our systems predict the behavior of pedestrians, cyclists, and other vehicles, then plan trajectories that are safe, comfortable, and legible to the people around them. We work at the intersection of machine learning, real-time systems, and large-scale data infrastructure — and everything we build runs on vehicles operating in real traffic.

## About the Role

You will design and train the models that anticipate what other road users will do next, and turn those predictions into driving decisions. This means working with petabytes of real driving data, building evaluation frameworks that actually correlate with on-road safety, and shipping models that run under hard latency budgets on embedded hardware.

This is a production engineering role. You will spend meaningful time on failure analysis, long-tail scenarios, and the gap between offline metrics and on-road behavior.

## What You'll Do

- Design, train, and deploy models for behavioral prediction and motion planning that run on vehicles in real traffic
- Model multi-agent interaction and temporal dynamics — how a merge, an unprotected left, or an occluded pedestrian actually unfolds
- Own the metrics: build evaluation frameworks that correlate with real on-road safety and performance, not just offline loss
- Diagnose long-tail failures from real driving logs and close the loop back into training data and model design
- Optimize trained models for real-time inference under strict latency, memory, and compute constraints on embedded hardware
- Build and maintain data pipelines that process, clean, and label large-scale vehicle sensor and simulation datasets

## What You'll Need

Domain experience (required):

- Hands-on experience with at least one of: behavioral or trajectory prediction, motion planning, decision-making under uncertainty, or closely adjacent autonomy work (navigation, SLAM, control, or perception-for-planning) for autonomous vehicles, mobile robots, drones, or comparable physical systems
- Experience deploying machine learning to real hardware operating in the physical world, under real-time or resource constraints. Simulation-only or offline-only experience does not meet this bar.

Engineering (required):

- Strong Python and production experience with a modern deep learning framework (PyTorch, TensorFlow, or JAX)
- Proficiency in C++ (or Rust) for performance-critical inference and integration code
- Demonstrated ownership of a system from prototype through deployment, including debugging it after it shipped

How we evaluate: We weight what you have actually built and shipped far more heavily than credentials. We regularly hire people without advanced degrees and without prior autonomous-vehicle experience. What we look for is specific, verifiable engineering work  systems you built, constraints you worked under, and failures you diagnosed and fixed

## #LI-MS1

Candidates are required to be authorized to work in the U.S. The employer is not offering relocation, sponsorship, and remote work options are not available.

Avride is an equal opportunity employer and committed to providing reasonable accommodations to qualified applicants and employees with disabilities to ensure they have equal access to employment opportunities. Avride complies with the Americans with Disabilities Act (ADA), if you need a reasonable accommodation to assist with the application or hiring process, or to perform the essential functions of a job, please email jobs@avride.ai.

## About Avride

## Company Overview
- **One-liner**: Avride is a developer of autonomous vehicle and delivery robot technology, providing self-driving taxi and last-mile delivery services in partnership with Uber.
- **Entity Type**: Private (subsidiary of Nebius Group)
- **Headquarters**: Austin, Texas, United States (additional offices in Tel Aviv, Israel; Belgrade, Serbia; Seoul, South Korea)
- **Founded**: 2020 (as a spin-off from Yandex’s self-driving car group, which began development in 2017)
- **Founders**: Originated from Yandex’s autonomous driving team; individual founders are not named publicly.

## Core Business
- **Primary industry**: Vehicular automation, autonomous mobility, and last-mile delivery robotics.
- **Target customers**: B2B (ride-hail platforms like Uber, automotive OEMs like Hyundai), B2C (riders using robotaxis via Uber app), and enterprise (restaurants and retailers using delivery robots through Uber Eats).
- **Mission**: To make roads safer and more sustainable by deploying autonomous technology that never tires, gets distracted, or breaks rules.

## Products & Services
- **Robotaxi Service**: Autonomous taxi fleet (based on retrofitted Hyundai Ioniq 5 vehicles) operating in a nine-square-mile area of downtown Dallas, bookable via the Uber app. As of April 2026, Avride has 200 robotaxis in service.
- **Delivery Robots**: Sidewalk robots that travel at 8 km/h (5 mph), equipped with lidars, cameras, and ultrasonic sensors. Currently delivering Uber Eats orders in Austin, Dallas, and Jersey City. Over 600,000 deliveries completed to date.
- **Autonomous Driver Platform**: A universal driving system (hardware and software) that can be adapted to different vehicle types, including passenger cars and delivery robots, leveraging shared technology for continuous improvement.
- **Proprietary Hardware**: Custom lidars, cameras, and computational hardware designed in-house to enhance performance and redundancy.

## Market Standing
- **Valuation / Market Cap**: Not disclosed (private company).
- **Key Metric**: Total funding of **$425M** across two rounds:
  - $50M convertible note (September 2020)
  - $375M venture round (October 2025) — 2 investors.
- **Notable Investors / Partners**: Nebius Group (parent company); strategic partners include **Uber** (robotaxi and delivery integration) and **Hyundai** (robotaxi development partnership).
- **Growth Signals**:
  - Headcount of **283 employees** (+75.4% year-over-year).
  - 13,541 LinkedIn followers (+80.3% annual growth).
  - Over 250,000 customers have used services powered by Avride technology.
  - Expanded from initial testing in Austin and Seoul to live commercial operations in Dallas, Jersey City, and South Korea.
  - NHTSA logged 37 accidents involving Avride vehicles (as of March 2026) and opened a safety probe in May 2026 — a sign of regulatory scrutiny that often accompanies scaling autonomous fleets.

## Competitive Advantages
- **Proven real‑world deployment**: Operating robotaxis and delivery robots in dense urban environments across the U.S. and South Korea.
- **Unified technology stack**: The same autonomous software and hardware power both cars and delivery robots, enabling cross‑learning and faster iteration.
- **Strong partnership ecosystem**: Exclusive tie‑ups with Uber (rideshare and delivery) and Hyundai (OEM collaboration) provide immediate go‑to‑market channels.
- **In‑house hardware**: Custom sensors and compute units reduce dependency on third‑party suppliers and optimize performance for specific platforms.
- **Safety‑first philosophy**: 360‑degree perception, redundant systems, and thousands of hours of simulation and real‑world testing.

## Strategic Focus
- **Scaling robotaxi operations**: Expanding the Dallas service area and launching in additional U.S. cities in 2026–2027.
- **Deepening OEM partnerships**: Collaborating with Hyundai on next‑generation robotaxis.
- **Broadening delivery robot coverage**: Entering new markets with Uber Eats, including potential international expansion.
- **Growing the engineering team**: Actively hiring for roles in software, hardware, simulation, and operations (14 open positions as of mid‑2026).

## Why Work Here
- **Cutting‑edge autonomy**: Solve some of the hardest problems in perception, planning, and real‑time control for both cars and robots.
- **Large, experienced team**: Over 200 engineers, many from Yandex’s pioneering autonomous driving group, with deep domain knowledge.
- **Fast‑paced, high‑impact environment**: Work on technology used by hundreds of thousands of real customers.
- **Global presence**: Offices in Austin (HQ), Tel Aviv, Belgrade, and Seoul offer opportunities for international collaboration.
- **Culture of ownership**: Flat structure with small cross‑functional teams; engineers own features end‑to‑end.
- **Work policy**: Not explicitly stated, but roles span on‑site (operations), hybrid, and remote‑friendly positions depending on function.

## Sources
1. [avride.ai](https://www.avride.ai/)
2. [Avride About page](https://www.avride.ai/about)
3. [Avride Careers page](https://www.avride.ai/careers)
4. [Avride LinkedIn](https://www.linkedin.com/company/avrideai)
5. [Avride Wikipedia](https://en.wikipedia.org/wiki/Avride)

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