Head of Machine Learning
Summary
Leads ML strategy, architecture, and team for a product company, designing signal processing and ML systems for production, optimizing model performance, and managing MLOps pipelines.
- Define and execute the machine learning strategy aligned with product and business objectives.
- Lead the design and evolution of signal processing and machine learning architectures for production systems.
- Establish technical standards, best practices, and development processes for ML systems.
- Evaluate emerging ML technologies and identify opportunities to enhance product capabilities.
- Provide technical leadership on architecture decisions, model selection, and system performance optimization.
- Oversee development, validation, deployment, and lifecycle management of machine learning models.
- Oversee the design, optimization, and scalability of signal processing pipelines.
- Define model performance metrics and drive improvements through evaluation and experimentation.
- Ensure robustness, maintainability, and scalability of production ML infrastructure, data pipelines, and supporting databases.
- Oversee MLOps practices, including model versioning, reproducibility, monitoring, and continuous improvement.
- Establish standards for dataset acquisition, quality, governance, and lifecycle management.
- Lead field data collection initiatives and expand/refine training datasets.
- Innovate data labeling, preprocessing, quality assurance, and representativeness methodologies.
- Collaborate with Product Management, Engineering, and executive leadership on the AI roadmap and development priorities.
- Translate customer needs and operational challenges into ML solutions and product capabilities.
- Provide technical leadership during customer demonstrations, field trials, and critical deployments.
- Serve as the organization’s machine learning subject matter expert.
- Lead and mentor a high-performing machine learning team.
- Establish project priorities, resource allocation, and development plans.
- Drive project execution through planning, risk management, and Jira.
- Define engineering processes, conduct technical reviews, and promote knowledge sharing.
Requirements
- Bachelor’s or Master’s degree in Engineering, Computer Science, Mathematics, Physics, or a related field.
- 5–10 years of experience in machine learning, AI, and software development.
- Experience with AWS.
- Experience with Claude.
- Ability to write in C for embedded systems.
- Proficiency in Python and scripting.
- Ability to convert algorithms to code and apply machine learning concepts such as decision trees, logistic regression, and Bayesian analysis to complex datasets.
- Proven track record leading machine learning teams and delivering quality products.
- Experience with embedded ML on hardware or IoT devices.
- Experience translating real-world applications and customer needs into ML solutions.
- Strong proficiency in Python, including PyTorch and Scikit-learn.
- End‑to‑end ML project experience covering data pipelines, data cleaning, preprocessing, model design, training, validation, and deployment.
- Experience with project management tools, including JIRA.
- Experience with cloud platforms such as AWS or Azure.
- Strong technical communication, documentation, and organizational skills.
Core Competencies
Demonstrates expertise in machine learning strategy, architecture design, and MLOps practices, with a strong focus on model performance optimization and data pipeline management. Proven ability to lead high‑performing teams and translate customer needs into effective ML solutions.
Highest‑signal resume keywords
- Machine Learning Strategy
- MLOps Practices
- Python Proficiency
- Embedded Systems Development
- Project Management with JIRA
ATS Optimization Keywords
Hard Skills
- Machine Learning
- Signal Processing
- Model Selection
- Data Pipeline Management
- Algorithm Development
- C Programming
- Decision Trees
- Logistic Regression
- Bayesian Analysis
- Data Cleaning
Soft Skills
- Technical Leadership
- Organizational Skills
- Technical Communication
- Mentoring
- Collaboration
Industry Keywords
- Machine Learning Models
- Data Governance
- Embedded ML
- IoT Devices
- Field Data Collection
Tools & Technologies
- AWS
- Azure
- JIRA
- PyTorch
- Scikit-learn