Senior Software Engineer - Prediction

Latitude AI; Marine City (MI); On-site; USD 179,000 - 269,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Health Savings Account with employer-m

401(k) with immediate vesting

Life insurance

Parental leave

Unlimited vacation

15 paid holidays

Pre-tax commuter benefits

Wellness stipend

Professional development reimbursement

Employee assistance program

Discounts and perks

Job summary

Latitude AI is advancing Ford’s autonomy roadmap with an in-house ADAS platform; we’re building an autonomous driving system that understands, predicts, and responds to complex traffic scenarios in real time. You’ll contribute to model inference, latency optimization, and scalable software architecture while collaborating with teams across ML, robotics, and safety engineering.

You will work on production-quality code, optimize modern ML models for constrained hardware, and help deploy

Qualifications

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, Robotics or related field with 4+ years of relevant experience (or Master's degree and 2+ years of relevant experience, or PhD)
• Fluency in modern C++ including good software design skills and experience with Python
• Familiarity with PyTorch, ONNX and deployment/optimization of models is desired
• Knowledgeable in areas pertaining to probabilistic inference, probability theory, statistics
• Application of Machine Learning in classification, regression, time series forecasting
• Willingness to dive into anything, but know when to ask for cross team collaboration
• Industry experience writing production-quality, performance-critical code, and maintaining large codebases is desired
• Strong written and verbal communication skills are required

Responsibilities

• Integrate and optimize algorithms and models for predicting the future behavior of other traffic participants in real time and planning a proper response
• Measure the statistical properties and quality of predictions and plans
• Build a system that safely handles nominal and out-of-distribution driving events
• Evaluate and verify the system's performance on real-world and simulated data
• Improve and deliver reliable software through continuous integration, automated testing and code reviews
• Collaborate across different teams to jointly achieve objectives in improving system performance
• Monitor system performance and ensure designs and implementation stay within resource constraints

Skills

C++

Python

Communication

Education

Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, Robotics or related field

Master's degree or PhD preferred

Tools

PyTorch

ONNX

Job description

Latitude AI (lat.ai) is building the future of Ford’s autonomy roadmap to make travel safer, less stressful, and more enjoyable for everyone. Bringing this vision to scale, our fully in-house developed hands-free ADAS platform will debut on the all-new Ford Fathom in 2027.

When you join the Latitude team, you’ll work alongside leading experts across machine learning and robotics, cloud platforms, mapping, sensors and compute systems, test operations, systems and safety engineering – all dedicated to redefining the relationship between people and their vehicles for millions of customers.

As a Ford Motor Company subsidiary, we operate independently to develop automated driving technology at the speed of a technology startup. Latitude is headquartered in Pittsburgh with engineering centers in Dearborn, Mich., and Palo Alto, Calif.

Meet the team:

The Latitude AI Autonomy Behavior team is responsible for designing and building systems that allow an autonomous vehicle to understand the current scene around it, predict how the scene will evolve in the future and make decisions on how to respond. The team focuses on deploying SOTA algorithms in limited compute environments, and writing efficient software architectures that take advantage of hardware optimizations.

You will play a key role in improving the quality and safety of the autonomy system through the optimization and integration of both modern (transformer, etc…) and classical ML (Random Forest, SVM, etc..) models. Our models utilize time series data to solve regression, generation, and classification tasks. Improving model inference and latency on our target hardware allows us to deploy deeper and more powerful models and improve the vehicle’s ability to reason in complex situations.

What you’ll do:

• Integrate and optimize algorithms and models for predicting the future behavior of other traffic participants in real time and planning a proper response
• Measure the statistical properties and quality of predictions and plans
• Build a system that safely handles nominal and out-of-distribution driving events
• Evaluate and verify the system's performance on real-world and simulated data
• Improve and deliver reliable software through continuous integration, automated testing and code reviews
• Collaborate across different teams to jointly achieve objectives in improving system performance
• Monitor system performance and ensure designs and implementation stay within resource constraints

What you'll need to succeed:

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, Robotics or a related field and 4+ years of relevant experience (or Master's degree and 2+ years of relevant experience, or PhD)
• Fluency in modern C++ including good software design skills and experience with Python
• Familiarity with PyTorch, ONNX and deployment/optimization of models is desired
• Knowledgeable in areas pertaining to probabilistic inference, probability theory, statistics
• Application of Machine Learning in classification, regression, time series forecasting
• Willingness to dive into anything, but know when to ask for cross team collaboration
• Industry experience writing production-quality, performance-critical code, and maintaining large codebases is desired
• Strong written and verbal communication skills are required

What we offer you:

• Competitive compensation packages
• High-quality individual and family medical, dental, and vision insurance
• Health savings account with available employer match
• Employer-matched 401(k) retirement plan with immediate vesting
• Employer-paid group term life insurance and the option to elect voluntary life insurance
• Paid parental leave
• Paid medical leave
• Unlimited vacation
• 15 paid holidays
• Daily lunches, snacks, and beverages available in all office locations
• Pre-tax spending accounts for healthcare and dependent care expenses
• Pre-tax commuter benefits
• Monthly wellness stipend
• Adoption/Surrogacy support program
• Backup child and elder care program
• Professional development reimbursement
• Employee assistance program
• Discounted programs that include legal services, identity theft protection, pet insurance, and more
• Company and team bonding outlets: employee resource groups, quarterly team activity stipend, and wellness initiatives

Learn more about Latitude’s team, mission and career opportunities at lat.ai!

The expected base salary range for this full-time position in California is $179,200 - $268,800 USD. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Latitude employees are also eligible to participate in Latitude’s annual bonus programs, equity compensation, and generous Company benefits program, subject to eligibility requirements.

Candidates for positions with Latitude AI must be legally authorized to work in the United States on a permanent basis. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is available for this position.

We are an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status.