Okay, I've searched for Staff or Principal Machine Learning Engineer roles in Ann Arbor, MI within the autonomous vehicles industry, requiring 5+ years of experience, full-time positions, and a master's degree in Computer Science or related fields. I have found 23 positions matching your criteria.

Sure. Here's the analysis:

Job Analysis:

This role fundamentally centers on architecting and advancing sophisticated machine learning models that enable perception, prediction, and decision-making for autonomous trucking systems. The position targets experienced engineers who can extend and innovate deep learning techniques across multiple sensor modalities (radar, lidar, vision) to solve complex problems of object detection, spatial reasoning, behavior prediction, and trajectory planning. Success here means delivering robust, production-quality ML models integrated seamlessly into a broader autonomy stack, often operating under real-time constraints in safety-critical environments. Beyond technical prowess, this role demands autonomy in designing scalable training pipelines handling diverse and partially annotated data, rigorous performance analysis, and proactive innovation to keep the company at the forefront of AV technologies. Collaboration is vital: the candidate must work cross-functionally with robotics, data engineering, and hardware teams to ensure coherent system integration, while also providing leadership by driving ML strategic roadmaps and mentoring others. The role’s requirements—advanced degrees, several years in applied ML particularly in robotics or autonomous systems, proficiency in Python and PyTorch, and ideally CUDA and distributed computing experience—are designed to ensure candidates can bridge research breakthroughs with engineering rigor and real-world deployment. The implied challenges include navigating the inherent uncertainties of real-world data, maintaining model reliability for safety-critical operations, and anticipating future AV industry trends to shape innovative solutions. Early success would be demonstrated by developing scalable training workflows, improving model robustness, and influencing architectural decisions aligned with Torc’s autonomy goals.

Company Analysis:

Torc Robotics occupies a distinctive niche as a pioneer and mature technology leader in autonomous trucking, reinforced by its strategic position under Daimler, North America's dominant heavy-duty truck manufacturer. This backing provides the company with substantial resources, domain expertise, and direct access to manufacturing insights, embedding the ML engineer’s work deeply into a tangible, impactful transportation ecosystem. The culture, based on Torc’s descriptions and perks, appears collaborative, inclusive, and innovation-driven, with an emphasis on team energy and mentorship, which is critical in a fast-evolving technology field that balances cutting-edge research with deployment realities. Torc’s mission emphasizes safety and efficiency in freight transport, signaling an organizational priority on building reliable, real-world autonomous systems that improve public infrastructure and commerce. The role’s placement within the Model Development team situates it as a strategic, high-visibility contributor working across departments and reporting to senior technical leadership—meaning individuals here will influence both day-to-day technical direction and long-term product roadmaps. This is a forward-looking, growth-oriented hire pipeline to secure top-tier talent who can sustain and accelerate innovation. For a candidate, success means not only technical excellence but also aligning with Torc’s ambition to lead the future of freight movement through automation, making this role both highly impactful and closely tied to corporate vision.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you led a machine learning project that required integrating multiple sensor modalities. How did you manage the challenges involved?
• Behavioral: Describe an experience where you had to mentor junior engineers or lead a cross-functional technical effort. What approach did you take to ensure success?
• Behavioral: Can you share an instance when you encountered ambiguous or incomplete training data? How did you deal with it to still deliver a robust ML solution?
• Behavioral: Give an example of when you had to influence architectural or strategic decisions with your ML expertise. How did you communicate your recommendations?
• Technical: How would you design and implement a deep learning model for multi-modal object detection in an autonomous vehicle context?
• Technical: Describe your experience with optimizing large-scale training pipelines, including data preprocessing and metrics tracking. What tools or frameworks have you found most effective?
• Technical: Explain how you approach debugging and improving model performance in environments with partially labeled or noisy data.
• Technical: What is your experience with CUDA programming or creating custom PyTorch operators? How did that impact your ML model deployment?
• Situational: Imagine a scenario where a perception model shows degraded performance after integration with the vehicle’s autonomy stack. How would you diagnose and address the issue?
• Situational: You are tasked with developing a trajectory prediction model but have limited annotated datasets for rare edge cases. How would you enhance model robustness in this situation?
• Situational: Assume conflicting priorities arise between hardware acceleration constraints and model accuracy. How do you balance these competing demands?
• Company Fit: Why are you interested in building autonomous trucking software specifically at Torc Robotics and how do you see yourself contributing to our mission?
• Company Fit: Torc emphasizes collaboration across teams and inclusive culture. How do you ensure effective communication and teamwork in complex, interdisciplinary projects?
• Company Fit: What excites you most about working in a company backed by a major truck manufacturer like Daimler, and how does this influence your approach to ML engineering?
• Candidate Questions: Can you describe the structure of the Model Development team and how it interacts with other departments like robotics and hardware?
• Candidate Questions: What are the current biggest technical challenges the ML team is facing in advancing Torc’s autonomous driving capabilities?
• Candidate Questions: How does Torc support continuous learning and innovation for ML engineers to stay ahead in this fast-evolving field?