Engineering Manager, Machine Learning

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Requirements:

• 5+ years of experience managing a software engineering team and a proven track record.
• 2+ years of experience using Cloud platforms (GCP, AWS, etc),
• Background in Machine learning frameworks (such as PyTorch), and data processing tools (Spark, Pandas, etc.) are a big bonus but not mandatory.
• You are proficient in software development practices, including specing, documenting, testing, reviewing, deploying, and monitoring, and are passionate about writing clean, defensible code.
• Excellent communication and collaboration skills to work effectively with cross-functional teams, including software engineers, data engineers, product managers, execs, and other stakeholders.
• You are flexible and capable of working with a diverse team with various communication styles and needs. You are empathic and supportive.
• You create equitable processes across the entire employee lifecycle. You are particularly mindful of addressing the needs of marginalized people.
• Experience in text-to-speech, speech-to-text, audio libraries, or cloud storage is a bonus.

Responsibilities:

• Build and lead a high-performing and collaborative machine learning team, fostering an environment of innovation, growth, and accountability.
• Collaborate with Product Management and other stakeholders to define, prioritize, and deliver the ML roadmap, aligning it with business goals and technical feasibility.
• Ensure that complex projects are effectively scoped, planned, executed, and delivered on time while meeting cross-team quality standards.
• Oversee multiple concurrent machine learning projects simultaneously, facilitating planning, and generally ensuring that deliverables are met on time.
• Own and run team standups, sprint planning, and other delivery ceremonies.
• Work one-on-one with team members to meet their needs, providing regular and constructive feedback. You’ll show concern and success for the well-being of others and do what you can to support people.
• Conduct design, code and pull request reviews with the team.
• Champion best practices in machine learning and software engineering including testing, documentation, readability, defensibility, etc.
• Manage team resources effectively by prioritizing projects based on impact while balancing budget constraints for infrastructure (e.g., compute resources) or tooling needs.
• Foster customer empathy within the team by introducing real-world use cases, facilitating discussions on customer pain points, reviewing feedback loops, and ensuring alignment with user needs.
• All together you’ll foster an inclusive team culture and working environment.