Description review
Lead Machine Learning Engineer
Serve Robotics · Canada, USA · back to the listing
HR standards
58/100
needs work
Title ↔ description
82/100
solid
Reads as
Machine Learning Engineer
100% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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What the listing never says
- 19 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
- No location or timezone policy stated, so a candidate cannot tell where they may work from. Scope clarity
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
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, Miami, Dallas, Atlanta and Chicago 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.
This role develops and scales large-scale machine learning training systems for multimodal robotics data, enabling the creation of high-performance autonomy models. By optimizing distributed training pipelines, neural network architectures, and data processing workflows, the position improves training efficiency, accelerates model iteration, and maximizes GPU utilization. The role collaborates closely with ML researchers and infrastructure teams, influencing the design, deployment, and performance of end-to-end autonomy models and the large-scale data pipelines that support them.
Responsibilities
•
Design and maintain training systems that can process and learn from petabyte-scale multimodal datasets (e.g., video and point cloud data). This includes ensuring data is efficiently loaded, distributed, and processed across large GPU clusters.
•
Identify and resolve bottlenecks in the training pipeline, including data loading, preprocessing, model computation, and inter-node communication, to maximize GPU utilization and reduce training time.
•
Work with the ML team to develop and refine neural network architectures suitable for autonomy tasks, particularly those handling high-dimensional and sequential sensor data.
•
Create and adjust loss functions and training strategies that help the model learn effectively from complex multimodal inputs and improve autonomy performance.
•
Configure, monitor, and maintain large-scale distributed training jobs across multiple machines and GPUs, ensuring stability, fault tolerance, and efficient resource usage.
•
Implement scalable systems to preprocess, transform, and augment large robotics datasets so that they are suitable for model training.
•
Work closely with ML scientists and other engineers to integrate new models, experiments, and training approaches into the production training pipeline.
•
Analyze training metrics, model outputs, and experiment logs to assess model performance and guide improvements in architecture, data usage, or training strategies.
•
Develop tools and workflows that allow teams to run experiments, track results, and iterate quickly on new model ideas or training approaches.
Qualifications
•
Master’s or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a closely related technical discipline.
•
Minimum of 5 years of professional experience developing, training, and deploying machine learning models in production environments.
•
Hands-on experience training machine learning models across multiple GPUs or compute nodes, including familiarity with distributed training frameworks and large dataset handling.
•
Strong programming skills in Python for implementing machine learning models, data pipelines, and training workflows.
•
Solid knowledge of core concepts such as neural networks, optimization algorithms, loss functions, model evaluation, and training methodologies.
What Makes You Stand out
•
Experience identifying and resolving training bottlenecks related to compute utilization, memory usage, and data throughput in machine learning systems.
•
Experience training machine learning models on robotics or autonomous driving datasets involving multimodal sensor inputs such as camera video, LiDAR point clouds, radar, or telemetry data.
•
Experience developing models that combine multiple data modalities (e.g., images, point clouds, and structured sensor data) into a unified learning system.
•
Peer-reviewed publications or significant research contributions in machine learning, robotics, or related areas.
*Please note: The listed base salary range applies to candidates based in the US. Compensation may vary depending on location, experience, and role alignment. We are open to qualified candidates working remotely in Canada
•
Canada - ALL: $177k - $215k CAD
The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles, Miami, Dallas, Atlanta and Chicago 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.
This role develops and scales large-scale machine learning training systems for multimodal robotics data, enabling the creation of high-performance autonomy models. By optimizing distributed training pipelines, neural network architectures, and data processing workflows, the position improves training efficiency, accelerates model iteration, and maximizes GPU utilization. The role collaborates closely with ML researchers and infrastructure teams, influencing the design, deployment, and performance of end-to-end autonomy models and the large-scale data pipelines that support them.
Responsibilities
•
Design and maintain training systems that can process and learn from petabyte-scale multimodal datasets (e.g., video and point cloud data). This includes ensuring data is efficiently loaded, distributed, and processed across large GPU clusters.
•
Identify and resolve bottlenecks in the training pipeline, including data loading, preprocessing, model computation, and inter-node communication, to maximize GPU utilization and reduce training time.
•
Work with the ML team to develop and refine neural network architectures suitable for autonomy tasks, particularly those handling high-dimensional and sequential sensor data.
•
Create and adjust loss functions and training strategies that help the model learn effectively from complex multimodal inputs and improve autonomy performance.
•
Configure, monitor, and maintain large-scale distributed training jobs across multiple machines and GPUs, ensuring stability, fault tolerance, and efficient resource usage.
•
Implement scalable systems to preprocess, transform, and augment large robotics datasets so that they are suitable for model training.
•
Work closely with ML scientists and other engineers to integrate new models, experiments, and training approaches into the production training pipeline.
•
Analyze training metrics, model outputs, and experiment logs to assess model performance and guide improvements in architecture, data usage, or training strategies.
•
Develop tools and workflows that allow teams to run experiments, track results, and iterate quickly on new model ideas or training approaches.
Qualifications
•
Master’s or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a closely related technical discipline.
•
Minimum of 5 years of professional experience developing, training, and deploying machine learning models in production environments.
•
Hands-on experience training machine learning models across multiple GPUs or compute nodes, including familiarity with distributed training frameworks and large dataset handling.
•
Strong programming skills in Python for implementing machine learning models, data pipelines, and training workflows.
•
Solid knowledge of core concepts such as neural networks, optimization algorithms, loss functions, model evaluation, and training methodologies.
What Makes You Stand out
•
Experience identifying and resolving training bottlenecks related to compute utilization, memory usage, and data throughput in machine learning systems.
•
Experience training machine learning models on robotics or autonomous driving datasets involving multimodal sensor inputs such as camera video, LiDAR point clouds, radar, or telemetry data.
•
Experience developing models that combine multiple data modalities (e.g., images, point clouds, and structured sensor data) into a unified learning system.
•
Peer-reviewed publications or significant research contributions in machine learning, robotics, or related areas.
*Please note: The listed base salary range applies to candidates based in the US. Compensation may vary depending on location, experience, and role alignment. We are open to qualified candidates working remotely in Canada
•
Canada - ALL: $177k - $215k CAD