Engineering Manager

hace 1 mes

Barcelona, España sennder A tiempo completo

Machine Learning (ML) and Artificial Intelligence(AI) are revolutionizing the way of doing business at a global scale. sennder is a European digital freight forwarder with a data-centric problem-solving approach to build the next generation of supply chain and road logistics services. Do you want to help us to shape the future?

We are looking for a (Staff) Engineering Manager - Machine Learning to join our central ML Recommendations team - as part of **sennAI** department. The department's mission is to achieve "Automated & Data-Driven Road Logistics". We're a large, diverse and multidiscplinary group of ML&AI engineers, data scientists, backend/frontend engineers and technical product people that are passionate by the new AI-empowered digitalization wave that is changing our world. We want to attract, retain and grow world-class talent to form a incredible group that can provide you the most productive and growth-friendly time of your career.

**sennAI** purpose is to build proprietary technology that can automate sales, brokerage and other businessrelated activities. Such automation can enable a flywheel where data acquisition and revenues grow exponentially with one another. The scope of our teams is creating best-in-class predictive analytics services while approaching ML Engineering in an holistic, end-to-end fashion: from best practices in ML modelling until engineering excellence around our MLOps Platform that lifts the developer experience to a different realm.

Every day, we acquire 3M+ new real-time data points (augmenting by the day) about the road logistics industry in Europe. This data is used to build the future of logistics marketplaces where pricing optimization, load-to-carrier recommendation, load search and network optimization happen in an automated fashion.

Can you even imagine where we can go with your help? Let's #keepOnTrucking... together

**YOUR MISSION**:

- As a people manager: Hire, onboard, manage, engage (including by organising team building events), coach, grow and retain exceptional talent in the areas of machine learning, data science and MLOps;
- As a leader: foster an environment of trust, self-organization, empowerment, Agile-first mindset, failurefriendly but mandatory-learnings towards to both personal and professional growth;
- As a individual contributor, you will hands-on address some of the following challenges (whenever time allows and promoting a culture of leading by example):

- Define the new state-of-the-art for machine learning engineering in the road logistics services;
- Prototype and subsequently operationalize innovative, data-intensive, end-to-end machine-learning-based decision engines, following the latest best practices on MLOps;
- Outline and develop health and performance monitoring tools (MLOps) of data pipelines and the machine learning services in production; 1/2
- Design and improve heterogeneous, asynchronous and high-performance large-data processing pipelines from/to multiple sources/destinations;
- Enforce the best principles in ML System Design by balancing the feedback loop on data exploitation and data acquisition, follow the 80/20 ruling, focus on the right metric in every design decision and once a shippable amount of value is created, go live, evaluate, learn and iterate;

**YOUR PROFILE**:

- Highly motivated with excellent communication and strong interpersonal skills;
- Proven experience in people management (onboarding, offboarding, individual development plans, performance review & management, hiring/letting go, 1-on-1s, continuous feedback, organizing team building events) in the tech industry (2+ yrs.);
- Proven experience in hands-on contribution to machine learning engineering teams in the tech industry (2+ yrs.);
- M.Sc or PhD in a quantitative field and/or working experience as a Data Scientist, Machine Learning Engineer or Data Engineer (MLOps);
- Teamaholic. We don't believe in super-heroes but rather in super-teams: teams that own products and are a single unit of work :)
- Experience with working on Machine Learning projects to learn from structured data, as well as, deep knowledge on statistics;
- Solid Python and software engineering skills, including best practices like CI/CD and Git;
- Experience with Agile philosophies (e.g.: Scrum, Scrumban, Kanban, XP) and project management tools (e.g.: JIRA);
- Basic understanding of machine learning product lifecycle and the commonly associated components (MLOps): Experimental Environment (e.g.: Jupyter Notebook, MLflow) Workflow management (e.g.: Airflow), Feature Stores (e.g.: Feast), DataOps/Pipelines (e.g.: Kafka), Model Deployment (e.g.: Terraform), Testing, Serving (e.g.: Docker, Flask). and Monitoring (e.g.: Datadog), Model Repository (e.g.: DVC) *
- Fluent written and verbal communication in English;

**BONUS**:

- Experience on MLOps in modern cloud systems (preferentially, AWS);
- Experience in managing remote and geographically distributed teams;
- List of tech

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