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Software Engineer, Machine Learning

At Serve Robotics, we’re reimagining how things move in cities. Our personable sidewalk robot is our vision for the future. It’s designed to take deliveries away from congested streets, make deliveries available to more people, and benefit local businesses. The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles while doing commercial deliveries.

The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles while doing commercial deliveries. We’re looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity.

Who We Are

We are tech industry veterans in software, hardware, and design who are pooling our skills to build the future we want to live in. We are solving real-world problems leveraging robotics, machine learning and computer vision, among other disciplines, with a mindful eye towards the end-to-end user experience. Our team is agile, diverse, and driven. We believe that the best way to solve complicated dynamic problems is collaboratively and respectfully.

What you'll be doing

Serve Robotics aims to develop dependable and proficient sidewalk autonomy software. We are looking for a cracked software engineer who understands AI fundamentals, industry experience in robotics or AV space, practical deep learning model training experience from a data-centric approach and has experience optimizing models to run in edge embedded hardware platforms.

Responsibilities

  • Identify, implement and fine-tune foundational vision grounding models that can be used to auto label large amounts of data for perception, prediction and mapping use-cases.

  • Train and deploy learning-based perception models using data-centric techniques for on-robot perception systems. Perception models should be able to do multi-modal learning capturing different semantics such as segmentation, object detection, scene understanding and tracking.

  • Optimize and accelerate machine learning models for deployment on embedded hardware platforms. Profile and analyze model performance, identify bottlenecks, and implement solutions to improve computational efficiency and reduce latency

  • Become an in-house expert in concepts knowledge distillation, quantization, model compression, and mixed-precision training.

  • Develop high-performance custom kernels using CUDA, or other low-level programming tools to accelerate computations and implement efficient algorithms.

  • Work with ML infrastructure engineers to assess and monitor model performance, analyze and resolve performance bottlenecks.

  • Produce high-quality code for software development, participate in code reviews to ensure the quality of code, and share knowledge with the team.

Qualifications

  • Master’s in Computer Science degree and 2+ years of industry experience with focus in ML/DL, Robotics, similar technical field of study, or equivalent practical experience

  • Proficient software engineer with 2+ years of production grade coding experience with C++, Python, CUDA programming.

  • Experience with edge-device perception stack deployment, experience with NVIDIA software libraries such as CUDA or TensorRT.

  • Fundamental understanding of computer vision, machine learning and deep learning basic concepts.

  • Experience pushing the boundaries of ML performance on hardware and has ideally worked on embedded platforms or autonomous systems before.

  • Experience working with multiple sensors such as Lidar, Mono/Stereo cameras, IMU, etc.

  • Strong communication skills.

What makes you standout

  • Demonstrated proficiency in tackling robotics and computer vision challenges within at least two of the following domains: multi-sensor feature extraction and fusion, object detection and tracking, 3D Estimation, and embodied AI with Transformer based models.

  • Open source project contributor.

  • Comfortable working with SQL queries and ETL logic for data ingress.

  • Experience with GCP or AWS, Kubernetes and Docker.

Average salary estimate

$135000 / YEARLY (est.)
min
max
$120000K
$150000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Software Engineer, Machine Learning , Serve Robotics

Join Serve Robotics as a Software Engineer specializing in Machine Learning and dive into the captivating world of robotics! Based in the vibrant city of Los Angeles, you’ll be part of a team dedicated to revolutionizing urban deliveries with our innovative sidewalk robots. At Serve Robotics, we’re not just creating machines; we’re crafting a future where technology meets everyday convenience. Your primary focus will be on building reliable, high-performance software that powers our autonomous robotic fleet. You'll get to work on foundational vision models designed to enhance perception and mapping capabilities, optimizing deep learning models for deployment on cutting-edge edge embedded hardware. You’ll collaborate with a diverse group of talented professionals to ensure our software performs efficiently under real-world conditions. An ideal candidate will have a solid understanding of AI fundamentals, combined with hands-on experience in robotics, providing you the opportunity to sharpen your skills in a dynamic environment. We invite forward-thinkers, problem-solvers, and tech enthusiasts to join us in making deliveries more accessible while benefiting local businesses and enhancing the urban landscape. If you're eager to push the boundaries of machine learning and robotics, let's build the future together at Serve Robotics!

Frequently Asked Questions (FAQs) for Software Engineer, Machine Learning Role at Serve Robotics
What are the main responsibilities of a Software Engineer, Machine Learning at Serve Robotics?

As a Software Engineer specializing in Machine Learning at Serve Robotics, your responsibilities will include fine-tuning foundational vision models to enhance perception, prediction, and mapping for our robotic fleet. You’ll train and deploy learning-based perception models while optimizing machine learning algorithms for efficient deployment on embedded hardware platforms.

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What qualifications do I need to apply for the Software Engineer, Machine Learning position at Serve Robotics?

To qualify for the Software Engineer, Machine Learning role at Serve Robotics, you should possess a Master’s degree in Computer Science or a related field, along with at least 2 years of industry experience focused on machine learning or deep learning within the robotics sector. Proficiency in programming languages such as C++ and Python, as well as experience with embedded platforms, is essential.

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What programming languages and tools should I be familiar with when applying for the Software Engineer, Machine Learning role at Serve Robotics?

Candidates applying for the Software Engineer, Machine Learning role at Serve Robotics should be proficient in programming languages like C++, and Python, and have experience with CUDA programming. Familiarity with NVIDIA software libraries, as well as tools used in deployment and optimization, such as TensorRT, will be beneficial.

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How does the Software Engineer, Machine Learning contribute to advancements in robotics at Serve Robotics?

The Software Engineer, Machine Learning plays a vital role at Serve Robotics by developing and optimizing algorithms that empower our robots to understand and interact with their surroundings effectively. Your expertise will directly contribute to improving the efficiency and reliability of robotic deliveries in urban environments.

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What makes a candidate stand out for the Software Engineer, Machine Learning role at Serve Robotics?

Candidates that have demonstrated proficiency in tackling robotics and computer vision challenges, particularly in areas such as multi-sensor feature extraction and fusion, or who have contributed to open-source projects will stand out for the Software Engineer, Machine Learning role at Serve Robotics. Experience with SQL, cloud services, and containerization tools also adds a competitive edge.

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Common Interview Questions for Software Engineer, Machine Learning
Can you explain your experience with machine learning models in embedded systems?

When responding to this question, detail your specific experiences optimizing machine learning models for embedded systems. Talk about the challenges you faced and how you overcame them, showcasing your technical knowledge as well as your problem-solving abilities.

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How do you approach optimizing machine learning models?

Describe your systematic approach to optimization. Focus on techniques like knowledge distillation, quantization, or model compression that you’ve used, and emphasize how these techniques improved the performance of your models on edge devices.

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What are the key considerations while developing computer vision algorithms for robotic systems?

Highlight the importance of accuracy, speed, and low computational costs when developing computer vision algorithms. Discuss your experiences working with various sensors and how you integrate and test new technologies within robotic systems.

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Can you describe a complex problem you solved in your previous roles related to machine learning or robotics?

Choose a relevant experience that showcases your analytical skills in tackling a complex problem. Provide context, the solution you implemented, and the outcome, emphasizing your role in the process.

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What experience do you have with multi-modal learning in machine learning?

Discuss any projects where you integrated multiple data sources for training models and how this approach improved model performance. Emphasize the value of multi-modal learning in enhancing perception and decision-making for robots.

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How do you ensure code quality in your software development process?

Discuss your practices for maintaining code quality, such as participating in code reviews, writing unit tests, and following coding standards. Highlight your commitment to collaborating with team members for continuous improvement.

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Explain a project where you utilized CUDA for performance enhancement.

Provide a specific example of a project where you implemented CUDA. Talk about how it improved computational efficiency and mention the results to demonstrate your understanding and application of parallel computing.

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What methods do you use to monitor model performance post-deployment?

Discuss your strategies for monitoring model performance, including tracking metrics and analyzing user feedback. Mention how you assess model performance over time and make adjustments based on real-world data.

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Can you share your experience with cloud platforms like GCP or AWS?

Be specific about the cloud services and tools you've used, such as deploying models, managing datasets, or utilizing machine learning services. Discuss how cloud environments influenced your development and deployment workflows.

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How do you stay updated with the latest developments in machine learning and robotics?

When answering, mention specific resources you follow, such as journals, conferences, online forums, and open-source communities. Highlight any workshops or training programs you've attended to enhance your skills.

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Why deliver a 2-pound burrito in a 2-ton car? Serve is the future of sustainable, self-driving delivery. Our zero-emissions rovers are designed to serve people in public spaces, starting with food delivery. We partner with platforms and merchants ...

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DATE POSTED
February 18, 2025

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