Principal Applied Scientist - Robotics

The IC4 Applied Scientist for Robotics, Perception, and Embodied AI will serve as a senior technical leader responsible for defining, prototyping, and delivering AI capabilities for commercially viable robotic systems. This role partners across science, software engineering, product, and hardware teams to identify high-impact opportunities, translate ambiguous product needs into research and engineering roadmaps, and guide end-to-end solution development from data collection and experimentation through production deployment. The scientist will lead applied research in multi-sensor fusion, real-time signals, perception, multimodal reasoning, reinforcement learning, and action-conditioned planning, with a strong bias for hands-on execution and full-stack delivery.

Key Responsibilities
Solution Identification:
–Independently collaborates with science, engineering, and product teams to identify business and product needs to create and implement solutions, promote innovation, and drive model implementations.
–Independently performs literature reviews and state-of-the-art analyses to understand the problem that needs to be solved.
–Creates a science plan to address product and/or business problems.
–Serves as a leading contributor on the identification of solutions based on the scientific process (e.g., including modeling approaches, evaluation techniques, data collections).
Solution Development – Applied Research:
–Develops and pilots research proofs-of-concept (POCs) or research experiments to demonstrate the feasibility of proposed solutions, gather supporting evidence, and secure resources for applied research.
–Independently identifies and prepares datasets for use in applied research. Ensures datasets meet quality standards, and identifies gaps in the data.
–Contributes to the design of and conducts research on new theories, tools, and methodologies to transfer and apply state-of-the-art technologies into products and services or make advancements in the field.
Solution Development:
–Designs, develops, and optimizes ML and AI technologies, such as model training/fine-tuning and computation optimizations.
–Designs and reviews the architecture of ML and AI solutions, such as data, model, training, and evaluation, employing best practices.
Solution Development – Articulating Research Insights:
–Independently identifies meaningful insights from data and metadata sources from analysis and experiments.
–Interprets and communicates insights and findings to cross-level and cross-functional audience.
–Creates patents, white papers, or other technical documentation related to applied research outcomes.
Solution Delivery – Coding and Documentation:
–Develops efficient, bug-free, medium-complexity code from scratch, and properly maintains and organizes existing codebase.
–Implements best practices for version control, code review, and code delivery/deployment.
–Builds and maintains professional documentation for technical processes (experimentation, data collection and analyses, model building).
–Tests and reviews code for bugs.
Solution Delivery:
–Collaborates with fellow technical teams to ensure the successful and timely delivery of solutions and integration of services.
–Collaborates with software engineers and/or machine learning engineers to deploy solutions into production environments.
–Participates in the peer review of solutions prior to delivery.
–Coordinates with team members for the delivery of solutions.
–Ensures delivered solutions successfully solve product and/or business problems.
–Independently collaborates with operational teams to evaluate and monitor solutions in production.
Building Research Capability – Research Excellence:
–Demonstrates advanced knowledge in at least one business-critical applied research technology area.
–Applies advanced domain knowledge and industry best practices to applied research.
Building Research Capability – Research Community Presence:
–Leads the publication and presentation of research findings and papers in conferences, research and industry outlets, etc.
–Participates in the scientific community by providing peer review and feedback.
–Establishes collaboration with internal research labs.

Core Responsibilities
Planning & Execution:
–Manages and coordinates moderately complex tasks, monitoring timelines and deliverables to ensure timely completion and adherence to requirements for a moderately sized project or initiative.
–Efficiently delegates, monitors, and prioritizes work across multiple projects, providing technical oversight and adjusting plans to address shifts in resources or timelines.
Collaboration & Partnership:
–Collaborates across the organization to align on expectations and achieve shared objectives.
–Leverages understanding of business leaders, stakeholders, and/or customers to ensure proposed solutions meet their needs.
–Supports inclusivity by actively seeking and listening to diverse perspectives, ensuring others feel heard and respected.
Problem Solving:
–Identifies and addresses moderately complex issues by analyzing a wide range of data and/or information to identify solutions in accordance with standard practices.
–Proactively escalates unresolved or critical issues with a thorough assessment and suggests potential solutions.
–Reviews, contributes to, and documents problem solving strategies.
Continuous Learning:
–Pursues learning opportunities to expand knowledge and skills and/or tools in new areas and stays abreast of the latest industry trends and best practices.
–Proactively seeks and leverages ongoing feedback and training to improve skills.
–Coaches and mentors junior team members, fostering continuous learning and knowledge sharing within and across teams.
Continuous Improvement:
–Develops ideas, recommends updates, and/or collaborates on the implementation of process improvements to increase the efficiency and effectiveness of processes, protocols, and workflows across teams, and evaluates the impact on key stakeholders.
–Solicits feedback from others on ideas for alternative approaches and methods for continued improvement.
Performance and Development:
–Contributes to the talent development pipeline by participating in candidate interviews, assessing candidates, and providing hiring recommendations.

Minimum Job Qualifications
Education and/or Experience:
11 years of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or related field

OR

Bachelor's Degree in Mathematics or Statistics, Computer Science, Data Science, Physics, or related field AND 7 years of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or related field

OR

Master's Degree in Mathematics or Statistics, Computer Science, Data Science, Physics, or related field AND 5 years of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or related field

OR

Doctorate in Mathematics or Statistics, Computer Science, Data Science, Physics, or related field AND 3 year of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or related field.

Job Skills:
Same skills as prior level plus;
Predictive Analytics Demonstrated expertise in building and applying predictive analytics to identify trends and inform strategic decisions.
Natural Language Processing Demonstrated ability to utilize NLP techniques and frameworks for advanced language data processing.
Statistical Analysis Demonstrated ability to conduct statistical analyses and interpret business or research data.
Machine Learning Frameworks Demonstrated ability to use machine learning frameworks to develop and optimize models for real-world business scenarios.
Generative Artificial Intelligence (GenAI) Model Building Demonstrated ability in or knowledge of GenAI model building, including training, fine-tuning, and monitoring generative AI solutions.
Data Analysis Demonstrated ability to analyze and interpret data to produce actionable business insights.
Research Demonstrated ability in or knowledge of research, including gathering information, formulating questions, and interpreting results to address objectives.
Research Planning and Literature Review Demonstrated ability in or knowledge of research planning and literature review, including comprehending and critiquing previous research to inform future work.

Programming Language:
3 years of experience in applicable programming language.

Preferred Job Qualifications
Education and/or Experience:
12 years of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or related field

OR

Bachelor's Degree in Mathematics or Statistics, Computer Science, Data Science, Physics, or related field AND 8 years of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or related field

OR

Master's Degree in Mathematics or Statistics, Computer Science, Data Science, Physics, or related field AND 6 years of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or related field

OR

Doctorate in Mathematics or Statistics, Computer Science, Data Science, Physics, or related field AND 4 years of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or related field.
Academic Writing and Publication:
3 years of experience creating publications (e.g., white papers, peer-reviewed conference or journal articles).