Engineering Manager

Who you are

• Leadership Experience: 3+ years of experience successfully managing and scaling an Engineering, Machine Learning, or Data Science team
• Technical Foundation: 7+ years of prior experience working as a Senior or Staff Individual Contributor, with deep, hands-on experience deploying applied Machine Learning models into production environments
• Domain ML Experience: Direct, hands-on experience building, deploying, and maintaining recommender systems, pricing algorithms, or complex statistical models in a production setting
• Technical Depth: You remain highly technically fluent. You possess strong proficiency in Python, SQL, and Git, and are deeply familiar with modern ML and data stacks (Snowflake, AWS, Datadog...). Experience utilizing Jupyter Notebooks for exploration, workflow orchestration (Airflow or Flyte), frameworks like TensorFlow (or PyTorch), and LLM tooling (LangChain) is required to effectively review code and deeply understand your team's day-to-day challenges
• Academic Background: A PhD in a Mathematical, Statistical, Computer Science, or related quantitative field is a strong plus
• Management Craft: High emotional intelligence with a strong track record of mentoring senior talent, driving career growth, and leading teams through complex, ambiguous technical landscapes
• Business Acumen: You live by "Outcome over Output." You have a sharp sense for commercial value, an ability to cut through noise, and the communication skills to expertly manage expectations with executive stakeholders and non-technical business leaders
• We welcome applications from individuals who may not meet every qualification listed but share our ambition and drive to make an impact. If you’re excited about this role, we encourage you to apply

What the job involves

• As the Engineering Manager for our Machine Learning & Data Science team, you will lead a highly talented group of Data Scientists and ML Engineers tackling some of the most complex operational challenges in logistics
• This is a dynamic, high-impact role where you will guide the strategic direction of our applied AI initiatives, while partnering closely with our Staff ML Engineer who owns the core technical architecture and technical leadership. You will champion a culture of excellence that balances rigorous engineering standards with the rapid delivery of business value
• Rather than just focusing on the "how" and the metrics of delivery (such as velocity, predictability, and engineering quality), you will be fundamentally driven by the "why"—ensuring your team’s output consistently translates into measurable business outcomes that drive real margin and efficiency gains for the company
• You will partner tightly with our AI Lead Product Managers to shape a high-ROI roadmap and collaborate closely with our Data & AI Platform (MLOps) team to ensure seamless deployment. Ultimately, your job is to create an inspiring, high-trust environment where top-tier engineers are empowered to build transformative AI products
• People Leadership & Culture: Manage, coach, and support a high-performing team of Data Scientists and ML Engineers. Foster a highly motivating, inclusive team spirit, creating an inspiring workplace where engineers feel empowered, challenged, and psychologically safe to innovate
• AI Adoption & Transformation: Champion our journey toward becoming a fully AI-native business. You will drive AI adoption within the department and beyond, continuously exploring how foundational models and agents can be leveraged to bring our internal workflows and internal products to the next level
• Technical Enablement: Maintain a deep connection to the technical reality of your team. While your primary focus is on people and roadmap execution, you will dive into the codebase during your ramp-up and occasionally contribute to tooling or non-critical path initiatives. You will use this context to seamlessly unblock your engineers and support the Staff Engineer's architectural vision
• Delivery & Execution: Drive agile execution and operational excellence. Guide the team in transitioning R&D prototypes into robust, scalable, production-ready ML systems, maintaining a high bar for engineering quality and predictability
• Roadmap & Business Value: Partner tightly with Product Managers to ruthlessly prioritize the backlog. Focus the team on initiatives with clear, measurable ROI, perfectly aligning technical possibilities with sennder's strategic commercial goals
• Cross-Functional Collaboration: Partner with your Staff ML Engineer to navigate deep technical challenges, and collaborate extensively with the Data & AI Platform team to align on global MLOps infrastructure, ensuring we build scalable and maintainable solutions