Sr. Simulation & Game Engine Engineer (Robotics)
Serve Robotics$170,000 - $220,000/year
Full Time
Senior (5 to 8 years), Expert & Leadership (9+ years)
Not SpecifiedCompensation
Senior (5 to 8 years)Experience Level
Full TimeJob Type
UnknownVisa
Automotive, AI, Robotics, Autonomous DrivingIndustries
Requirements
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field
- 8+ years of experience building data-intensive software systems, ideally in robotics, autonomous driving, or large-scale ML environments
- Proficient in Python, SQL, and familiar with C++
- Experience designing ETL pipelines using modern frameworks (e.g., Apache Spark, Flyte, Union)
- Strong knowledge of cloud-native architectures, including AWS services (e.g., S3, or equivalents like Google Cloud platform)
- Familiarity with sensor data types (camera, lidar, radar, GPS/IMU) and common data serialization formats (e.g., protobuf, ROS2bag, MCAP)
- Deep understanding of data quality, observability, and lineage in high-volume systems
- Track record of building reliable and performant infrastructure that supports both ad-hoc exploration and repeatable production workflows
- Bonus Qualifications
- Experience in AD/ADAS, robotics, or autonomous systems — especially handling perception or planning datasets
- Familiarity with ML pipeline orchestration frameworks (e.g., Kubeflow, SageMaker, etc.)
- Experience working with temporal or spatial data, including geospatial indexing and time-series alignment
- Exposure to synthetic data generation, simulation logging, or scenario replay pipelines
- Strong software engineering fundamentals, CI/CD, testing, code review, and service deployment best practices
- Experience collaborating with cross-functional, distributed teams across research and production orgs
Responsibilities
- Design and implement scalable, production-grade pipelines for data ingestion, transformation, storage, and retrieval from vehicle fleets and simulation environments
- Build internal tools and services for data labeling, curation, indexing, and cataloging across large and diverse datasets
- Collaborate with ML researchers, autonomy engineers, and data scientists to design schemas and APIs that power model training, evaluation, and debugging
- Develop and maintain feature stores, metadata systems, and versioning infrastructure for structured and unstructured data
- Support the generation and integration of synthetic datasets with real-world logs to enable hybrid training and simulation workflows
- Optimize pipelines for cost, latency, and traceability, ensuring reproducibility and consistency across environments
- Partner with simulation and cloud platform teams to automate workflows for closed-loop testing, scenario mining, and performance analytics
Skills
Data Pipelines
Data Ingestion
ETL
Feature Stores
Data Labeling
Metadata Systems
Versioning Infrastructure
APIs
ML Workflows
Simulation Data
Toyota Research Institute
Research in mobility, safety, and automation
About Toyota Research Institute
Toyota Research Institute focuses on improving mobility through research and development in the automotive and technology sectors. The company works on enhancing safety, automated driving, robotics, materials science, and machine learning. Their products include advanced safety features and automated driving systems that aim to make driving safer and more efficient. Unlike many competitors, TRI emphasizes a research-driven approach, collaborating with various partners and licensing their innovations to enhance Toyota's offerings and maintain a competitive edge. The goal of TRI is to advance mobility solutions that improve quality of life and support the transition to zero-emissions transportation.
Los Altos, CaliforniaHeadquarters
2016Year Founded
$100MTotal Funding
SEEDCompany Stage
Robotics & Automation, Automotive & Transportation, AI & Machine LearningIndustries
201-500Employees
Benefits
Highly competitive benefits package
Robust programs to support the wellbeing, happiness, and health of our people and their families.
401(k) plan including matching and annual profit sharing along with total vacation and holidays totaling 38 days per year.
Subsidized commuter benefits and generous employee and vehicle allowances
2 paid days per year to participate in volunteer activities.
Maternity Leave program with 10 paid weeks plus baby bonding leave and Milk Stork for traveling moms
Baby Bonding Leave–an additional 16 paid weeks–to all new parents, including those who choose to adopt.
Back-up child and adult / elder care programs to help everyone thrive
WellBeats Virtual Fitness Trainer.
Risks
Public skepticism may undermine AI-assisted driving safety initiatives.
Boston Dynamics' high costs could impact TRI's humanoid robot project returns.
Complexity of real-world environments may delay TRI's robot skill deployment.
Differentiation
TRI focuses on AI to enhance human life and mobility.
Partnerships with Boston Dynamics and Stanford showcase TRI's collaborative innovation.
TRI's Driving Sensei concept integrates AI to improve driver skills and engagement.
Upsides
Generative AI allows TRI's robots to learn complex tasks without new coding.
TRI's AI-driven material synthesis could revolutionize EV battery development.
Autonomous tandem drifting collaboration with Stanford pushes vehicle dynamics boundaries.
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