Engineering Manager
hace 2 semanas
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 an Engineering Manager - Machine Learning to join our central ML Recommendations team - as part of sennAI department.
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... togetherYOUR 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) \
- 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;
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