Staff Machine Learning Engineer
hace 3 semanas
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 Machine Learning Engineer to join our central Machine Learning Engineering teams - as part of sennAI department. The department’s mission is to achieve “Automated & Data-Driven Road Logistics”. We’re a large, diverse and multidisciplinary 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 business-related 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
IN THIS ROLE YOU WILL..
- Define the new state-of-the-art for machine learning engineering in the road logistics services
- Mentor junior to senior engineers, enabling them towards successful & impactful software deliveries
- Review technical roadmaps and deliveries across teams
- Design and develop health and performance monitoring tools (MLOps) of data pipelines and the machine learning services in production
- Lead design reviews with peers and stakeholders to decide amongst available technologies.
- Be hands-on when needed while review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- Lead the cross-team alignment effort on technical dependency finding and/or matching, cross-domain architectural design and all-in-all Artificial Intelligence related topics
- 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
WHAT WE ARE LOOKING FOR
- Highly motivated with excellent communication and strong interpersonal skills
- 4+ years of experience in deploying and maintaining in production data pipelines working at scale which are fueling and/or being fueled by machine learning models in production;
- Above-average Python software engineering skills, including best practices like CI/CD and Git
- 2+ years of experience with modern MLOps setups
- Teamaholic. We don’t believe in super-heroes but rather in super-teams: teams that own products and are the single unit of work : )
- Large experience with Agile philosophies (e.g.: Scrum, Scrumban, Kanban, XP) and project management tools (e.g.: JIRA)
- Consensus building mindset, big picture focus and ability to disagree and commit in order to establish a bias to action by default
- Solid understanding of machine learning product lifecycle and the commonly associated components (MLOps): Experimental Environment (e.g.: Jupyter Notebook, MLflow) Workflow management (e.g.: Air-flow), 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)
\*List of technologies/methodologies is for illustrative purposes. You are not expected to have experience in each single one of these technologies...but you are expected to know these challenges very well (as well as the associated solution spaces).
**Bonus**:
- Experience with the following MLOps stack: BentoML for model serving, Weights & Biases for Experimentation Platform, Ray for model training and Flyte for orchestration of ML Workflows.
- Top tier academic track on ML related conferences (e.g.: KDD, NeurIPS, ICML, AAAI, ECML/PKDD)
**WHAT YOU CAN EXPECT**:
At sennder, we want to maximize the individual potential of all employees and reinforce an inclusive culture and environment of c
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