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Sr. ML Research Scientist - Job Opportunity at Serve Robotics

Los Angeles, US
Full-time
Senior
Posted: August 8, 2025
On-site
USD 175,000 - 205,000 per year

Key Responsibilities

Design and develop next-generation learning-based prediction and planning pipelines that enable scalable deployment across an expanding autonomous robot fleet, directly impacting the company's ability to achieve commercial viability and market penetration
Lead research, prototyping, and implementation of cutting-edge machine learning algorithms including imitation learning and reinforcement learning for autonomy systems, positioning the company at the forefront of robotics innovation
Maintain expertise in latest advances in prediction and end-to-end modeling to ensure technological competitive advantage and inform strategic product development decisions
Drive the curation and management of diverse, real-world datasets that form the foundation for robust model training and validation across complex urban delivery scenarios
Analyze and optimize model performance across critical metrics including reliability, computational efficiency, and real-world deployability to ensure safe and cost-effective operations
Lead cross-functional collaboration initiatives to establish robust testing and validation processes that directly contribute to more intelligent and adaptable robot behaviors in production environments

Requirements

Education

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

Experience

5+ years of industry experience in building perception and prediction modules for robotics / AV stack

Required Skills

Hands-on experience with machine learning frameworks (TensorFlow, PyTorch, etc.) Exposure to state-of-the-art research or publications in perception and prediction approaches Strong background in sensor fusion (especially lidar and camera) and modern transformer-based model architecture Proven track record of building ML pipelines and deploying models in production Proficient in Python and C++, Someone who has a high bar to write production quality code Experience with large-scale real-world dataset curation and management
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Sauge AI Market Intelligence

Industry Trends

The autonomous delivery robotics market is experiencing rapid expansion driven by last-mile delivery challenges, labor shortages, and increasing consumer demand for contactless delivery solutions. Companies are shifting from proof-of-concept to commercial deployment phases, creating significant demand for experienced ML scientists who can bridge research and production systems. There is a notable industry-wide pivot toward end-to-end learning approaches and foundation models in robotics, moving away from traditional modular perception-planning-control pipelines. This trend is creating premium demand for researchers with expertise in transformer architectures, vision-language models, and multi-modal learning systems. Urban robotics deployment is becoming increasingly regulated, with cities implementing specific guidelines for sidewalk robots. This regulatory environment is driving demand for ML professionals who can ensure safety, reliability, and compliance in real-world deployment scenarios.

Salary Evaluation

The offered compensation range of $175,000-$205,000 is competitive for senior ML roles in Los Angeles robotics companies, though it sits slightly below premium tech companies like Waymo or Cruise which typically offer $220,000-$280,000 for similar roles. However, the equity potential in a growing robotics startup could provide significant upside compensation.

Role Significance

The role likely involves working within a 8-12 person AI/ML team, with direct collaboration across robotics, perception, and systems engineering teams. As a senior researcher, this person would likely mentor 1-2 junior researchers and coordinate with 3-4 cross-functional partners.
This is a high-impact senior individual contributor role with significant technical leadership responsibilities. The position requires someone who can operate independently on complex research problems while also driving cross-functional initiatives, indicating a role that bridges senior IC and technical leadership levels.

Key Projects

Development of next-generation prediction models for urban sidewalk navigation that can handle complex pedestrian interactions and dynamic obstacles Implementation of large-scale imitation learning systems that can leverage the company's fleet data to improve autonomous navigation capabilities Creation of robust evaluation frameworks for real-world model performance that ensure safety and reliability standards for commercial deployment

Success Factors

Deep technical expertise in modern ML architectures combined with practical deployment experience will be critical for translating research advances into production-ready systems that can operate reliably in complex urban environments Strong cross-functional collaboration skills are essential given the need to work closely with hardware engineers, systems teams, and product managers to ensure ML solutions integrate effectively with the broader robotics platform Ability to balance research innovation with practical constraints of real-world deployment, including computational efficiency, safety requirements, and regulatory compliance considerations Experience with large-scale data management and MLOps practices will be crucial for managing the continuous flow of real-world data from the robot fleet and maintaining model performance over time

Market Demand

High - Senior ML scientists with robotics and autonomous vehicle experience are in extremely high demand, with supply significantly lagging behind industry needs as more companies move from R&D to commercial deployment phases.

Important Skills

Critical Skills

Transformer-based architectures and modern deep learning frameworks are absolutely essential as the industry shifts toward end-to-end learning systems and foundation models for robotics applications Production ML pipeline experience is critical for this role since the company is already commercially deployed and requires systems that can operate reliably at scale with continuous model updates Sensor fusion expertise, particularly with lidar and camera data, is fundamental to building robust perception systems for urban navigation where multiple modalities are essential for safety Python and C++ proficiency with production-quality coding standards is essential for developing systems that integrate with real-time robotics platforms and meet safety-critical requirements

Beneficial Skills

Experience with foundation models, VLMs, and VLA architectures represents the cutting edge of robotics AI and would provide significant advantage for developing next-generation capabilities Cloud infrastructure expertise (GCP/AWS) and containerization technologies (Kubernetes/Docker) are increasingly important for managing large-scale robotics data and deploying models across distributed robot fleets Open source contributions demonstrate technical leadership and community engagement, which are valuable for a senior role requiring influence across technical teams Simulation and real-time testing experience provides crucial skills for validating ML models in controlled environments before real-world deployment, reducing development risk and iteration time

Unique Aspects

This role offers the rare opportunity to work on ML systems that are already deployed commercially, providing immediate real-world feedback and validation for research efforts, unlike many robotics positions that remain in R&D phases
The focus on sidewalk robotics presents unique technical challenges around pedestrian interaction prediction and urban navigation that differ significantly from traditional autonomous vehicle problems, offering specialized expertise development
The position combines cutting-edge research opportunities with practical deployment constraints, providing experience in both advancing state-of-the-art techniques and ensuring production reliability
Working with a robotics company that has achieved commercial deployment provides exposure to the full lifecycle of ML system development, from research through production monitoring and continuous improvement

Career Growth

Typical progression to principal/director level roles occurs within 3-5 years for high-performing senior researchers in the current market, accelerated by the high demand for robotics ML expertise and limited talent pool.

Potential Next Roles

ML Research Director or Principal Scientist roles focusing on robotics AI strategy and technical vision Head of AI/Autonomy positions at robotics startups or autonomous vehicle companies Technical leadership roles at major tech companies working on robotics initiatives (Google, Amazon, Apple) Founding technical roles at new robotics or AI companies, leveraging deep expertise in real-world ML deployment

Company Overview

Serve Robotics

Serve Robotics is a commercial-stage autonomous delivery robotics company that has successfully transitioned from R&D to active commercial operations in Los Angeles. The company focuses specifically on sidewalk delivery robots, representing a more targeted approach compared to broader robotics platforms, which allows for deeper specialization in urban navigation challenges.

Serve Robotics occupies a strong position in the last-mile delivery robotics market, with proven commercial traction and active fleet operations. While smaller than well-funded competitors like Starship Technologies, the company's focus on dense urban markets and established merchant relationships provides a differentiated market position.
Based in Los Angeles with active commercial operations, the company is strategically positioned in a major metropolitan market that provides rich real-world data and regulatory experience that can be leveraged for expansion to other urban markets across the United States.
The company culture emphasizes collaborative problem-solving and cross-functional teamwork, typical of successful robotics startups where hardware, software, and business challenges require integrated solutions. The focus on real-world deployment creates a results-oriented environment where theoretical research must translate to practical outcomes.
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