Latitude AI
Senior Software Engineer - Scalable Machine Learning
$179K–$269K
Pittsburgh, PA, Palo Alto, CA, Detroit, MI +4 · Remote · Senior · Full-time · Posted May 27, 2026
- Location
- Pittsburgh, PA, Palo Alto, CA, Detroit, MI
Dearborn, Michigan, United States
Palo Alto, California, United States
Pittsburgh, PA, United States
Remote - Experience
- 4+ years with a Bachelor’s degree; 2+ years with a Master’s degree; or PhD.
- Education
- Bachelor’s degree in Computer Engineering, Computer Science, Electrical Engineering, Robotics, or a related field; or Master’s degree and 2+ years of relevant experience; or PhD.
- Visa sponsorship
- Candidates must be legally authorized to work in the United States on a permanent basis. Visa sponsorship is available.
About the job
Designs and operates cloud-based machine-learning data pipelines, orchestration, and distributed training systems for autonomous-driving models.
What you'll do
- Build cloud services for data ingestion, workflow orchestration, and distributed training.
- Develop systems to generate, manage, and process large volumes of multi-sensor training data; advance MLOps practices for artifact management, experiment tracking, deployment, and testing.
- Collaborate across teams, integrate ML pipelines, and mentor junior engineers.
What they ask for
- Bachelor’s degree in a related field and 4+ years of relevant experience; or Master’s degree and 2+ years; or PhD.
- Track record delivering complex software projects from conception to production.
- Expertise in ML data pipelines, data generation, and distributed model training; experience with large-scale distributed systems and cloud infrastructure.
- Strong proficiency in Python and/or C++.
Nice to have
- Hands-on experience with distributed ML tools such as Ray, PyTorch, Dagster, LakeFS, or PyArrow.
- Experience with multi-sensor data fusion, safety-critical development, or L2/L3 autonomy.
Pay
$179,200 - $268,800 USD base salary
Tools
Python, C++, Ray, PyTorch, Dagster, LakeFS, PyArrow
AI focus
Improving the efficiency and scale of machine-learning training using large volumes of multi-sensor data; developing distributed training pipelines and MLOps capabilities for autonomous-driving models.
Model-written summary · the original posting is authoritative