Engineering Manager - Madrid, España - sennder

sennder

Empresa verificada

Madrid, España

hace 1 semana

Publicado por:

Isabel García

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Descripción

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, selforganization, empowerment, Agilefirst mindset, failurefriendly but mandatorylearnings towards to both personal and professional growth;
As a individual contributor, you will handson 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, dataintensive, endtoend machinelearningbased 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/
Design and improve heterogeneous, asynchronous and highperformance largedata 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, 1on1s, continuous feedback, organizing team building events) in the tech industry (2+ yrs.);
Proven experience in handson 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 superheroes but rather in superteams: 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)