Hayden AI

Full-stack Engineer

• Exhibit proficiency in software development, consistently delivering clean, efficient, and maintainable code.
• Contribute across all stages of Hayden’s Computer Vision (CV) and Machine Learning (ML) algorithm pipelines—from initial prototyping to production refinement—ensuring robustness, efficiency, and accuracy.
• Lead and execute comprehensive software testing efforts focused on CV/ML algorithms, spanning prototype validation, performance assessment, integration, and deployment readiness.
• Perform thorough root cause analysis on algorithm-related failures, identify trends in field behavior, and provide strategic recommendations to guide design and process improvements.
• Design, implement, and deploy cloud-based software pipelines for monitoring algorithm and device health across staging and production environments.
• Proactively identify systemic issues and drive resolution.
• Build and maintain cloud services to support the deployment of post-processing AI algorithms that enhance overall system accuracy, as well as internal tooling to aid algorithm development and performance analysis.
• Collaborate closely with device software, cloud, and algorithm teams to prototype and implement software changes that improve algorithm performance based on production data insights.

Algorithms Software Engineer

Machine Learning Engineer

• Architect, design, deploy, and operate scalable cloud-based MLOps platforms and workflows that enable efficient training, evaluation, deployment, monitoring, and lifecycle management of AI/ML models.
• Own the technical strategy and evolution of ML infrastructure, identifying architectural bottlenecks and driving cross-functional initiatives to improve developer productivity, experimentation velocity, scalability, and operational efficiency.
• Build robust, reliable, and automated systems that enable teams to ship new models and features rapidly while maintaining high standards for quality, reproducibility, observability, security, and production reliability.
• Define and implement infrastructure optimization strategies that balance performance, scalability, reliability, and cost across cloud and compute resources.
• Evaluate emerging tools, technologies, and industry best practices in MLOps, cloud infrastructure, and ML systems, and lead their adoption where they can meaningfully improve ML development and production workflows.
• Establish engineering best practices for ML infrastructure, including system design, code quality, testing, CI/CD, monitoring, documentation, and operational readiness.
• Provide technical leadership and mentorship to engineers, lead design and code reviews, and help raise the engineering quality and technical capabilities of the broader team.
• Partner closely with ML engineers, researchers, data engineers, and product teams to translate evolving AI/ML requirements into scalable and maintainable infrastructure solutions.
• Drive complex, ambiguous infrastructure projects from technical strategy and architecture through implementation, production deployment, and long-term operational ownership.

Senior Machine Learning Ops Engineer

Systems Engineer

• Tier 3 Escalation: Serve as the primary escalation point for complex, high-impact incidents that exceed standard field support scope.
• Root Cause Analysis: Perform deep-dive investigations into hardware, software, firmware, and network failures, delivering structured post-mortems with corrective action plans that drive permanent resolution.
• Operational Engineering: Partner with Product and Engineering teams to design and implement targeted enhancements that address recurring field issues at scale.
• Technical Documentation: Author runbooks, decision trees, and diagnostic workflows that empower Tier 1 and Tier 2 teams, and maintain a living knowledge base that evolves with the product.
• Production Activation: Oversee the end-to-end transition of newly installed units into active production, validate production readiness and coordinate activation before units go live on customer fleets.
• Strategic Rollouts: Support and execute the staged software and firmware updates across production and pilot fleets, monitoring post-deployment behavior and leading rapid response if regressions are detected.
• Cross-Functional Collaboration: Act as the engineering bridge between Field Operations, Product, and Engineering — translating field feedback and failure patterns into hardware/software improvements.
• Team Enablement: Deliver training sessions and technical briefings to field technicians and support staff to continuously raise the technical baseline across the organization.

Systems Engineer, Field Services

• Design, implement, and maintain software for edge devices, ensuring seamless integration with hardware
• Collaborate with hardware engineers to define system requirements and drive end-to-end feature delivery
• Optimize system performance for reliability, security, and efficiency in real-world operational environments
• Lead code reviews, establish software standards, and mentor junior engineers
• Troubleshoot complex field issues, working with cross-functional teams to provide rapid solutions

Senior Software Engineer – Device

• Lead the architecture, requirements gathering, and system-level design of integrated hardware-software solutions
• Drive end-to-end project execution, ensuring systems are delivered on-time and meet functional, operational, and quality targets
• Collaborate with cross-functional teams to align priorities and resolve technical challenges
• Interface with customers and internal stakeholders to translate operational requirements into actionable technical plans
• Ensure deployment readiness, supporting installations, configuration, and integration in the field
• Identify and troubleshoot system issues, root causes, and implement corrective actions
• Document design decisions, interfaces, and lessons learned for future scalability
• Contribute to process improvement across systems engineering and deployment practices

Staff Systems Engineer

• Exhibit proficiency in software development, consistently delivering clean, efficient, and maintainable code.
• Contribute across all stages of Hayden’s Computer Vision (CV) and Machine Learning (ML) algorithm pipelines—from initial prototyping to production refinement—ensuring robustness, efficiency, and accuracy.
• Lead and execute comprehensive software testing efforts focused on CV/ML algorithms, spanning prototype validation, performance assessment, integration, and deployment readiness.
• Perform thorough root cause analysis on algorithm- related failures, identify trends in field behavior, and provide strategic recommendations to guide design and process improvements.
• Design, implement, and deploy cloud-based software pipelines for monitoring algorithm and device health across staging and production environments; proactively identify systemic issues and drive resolution.
• Build and maintain cloud services to support the deployment of post-processing AI algorithms that enhance overall system accuracy, as well as internal tooling to aid algorithm development and performance analysis.
• Collaborate closely with device software, cloud, and algorithm teams to prototype and implement software changes that improve algorithm performance based on production data insights.

Software Engineer, Perception

• Come up with solutions to complex problems in perception using deep learning.
• Articulate ideas both verbally and in written form culminating in clear design and project plan docs for their work.
• Contribute to team roadmap and planning.
• Work with cross-functional teams across the company and contribute towards delivery of end to end solutions for the customer.

Deep Learning Engineer

Support Engineer

• Test development: Design and implement automated or manual test fixtures to support hardware validation and sustaining activities.
• Failure analysis: Probe, test, and debug returned hardware to identify root causes of failures. Conduct component-level troubleshooting using oscilloscopes, multimeters, and protocol analyzers.
• Reporting: Generate comprehensive first-level analysis reports and detailed FA documentation to be shared with cross-functional teams and vendors.
• Process improvement: Analyze RMA data trends to recommend design or firmware changes that reduce field failure rates.

Electrical Test Support Engineer

Electrical Engineer

• Lead electrical design of Edge AI products including but not limited to Automotive grade Edge AI computers and cameras while driving innovation and excellence.
• Champion collaboration cross functionally with mechanical, test, regulatory, manufacturing and installation teams by navigating the design from concept to launch.
• Develop product specifications, design circuits, perform design reviews and improve design.
• Generate working prototypes for proof of concept and customer demonstrations.
• Lead hands on involvement with building, debugging, and maintaining product builds.
• Develop test plans and processes, execute testing and guide validation of new designs.
• Own the electrical design part of the Edge AI system projects, strive for performance and improvements, seek out opportunities to make things work better - it's about continuous improvement.
• Ensure deliverables for the project are on track and stays within project budget.
• Partner with vendors and contract manufacturers to develop and manufacture electronic parts, modules and products.
• Work with the cross functional teams to get products certified for compliance.
• Lead and mentor electrical engineering team members, providing technical guidance, fostering professional development, supporting career growth, and ensuring high-quality execution across projects.

Manager, Electrical Engineering

• Lead the research, development, and deployment of state-of-the-art deep learning models for perception of urban scenes in production environments.
• Architect and optimize complex machine learning systems for scalability, efficiency, and robustness, utilizing cloud-native technologies.
• Develop and implement training techniques such as data augmentation or model distillation pipelines to improve model robustness under diverse urban and environmental conditions.
• Explore and integrate novel multi-modal foundational AI models, including large Vision-Language Models (VLMs), and develop strategies for their effective fine-tuning and adaptation to specific domain challenges.
• Drive innovation in model compression, quantization, and efficient inference techniques to optimize performance for both cloud and edge device deployments.
• Collaborate with cross-functional teams to define machine learning roadmaps, evaluate new technologies, and contribute to the overall technical strategy.
• Conduct research, and evaluate emerging deep learning techniques applicable to perception and intelligent mobility.