Senior Machine Learning Engineer - Sweden 🇸🇪

Modulai · posted Mar 10, 2021

Open to candidates in Sweden

Senior

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What this job asks for

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Must have

  • •5 years leading end-to-end ML projects
  • •Experience with ML infrastructure
  • •Experience with software development
  • •Excellent communicator
  • •Understanding of ML algorithms
  • •Passion for clean code

Nice to have

  • •Leadership in fast-growing organisations
  • •Product experience
  • •Deep learning frameworks and theory
  • •Data pipelining and infrastructure
  • •DevOps experience, CI/CD

Tools and technologies

Python, Scikit-learn, R, GCP, AWS, Azure, Apache Spark, Kubernetes

Worth checking before you apply

  • US-W2 only
Read the full posting

SENIOR MACHINE LEARNING ENGINEER (SMLE) - Sweden

As a member of Modulai you will be working with a broad range of problems with one common denominator – ML will be the key ingredient. As a SMLE you will be responsible for team success and advisory to both teams and clients.

In this role, you will be responsible for engaging clients and teams from the initial idea phase through to execution. We are looking for someone with a proven track record of successfully developing end-to-end ML products and leading teams in both commercial and delivery settings. It's important that you have a passion for ML and strong opinions on how to succeed in applied ML. At our company, being a lead is not just a formal title; it's a responsibility to help others achieve even greater success.

You will have to analyze the problem at hand, devise a solution strategy, and execute it. This typically entails gaining an in-depth understanding of the challenge, understanding the available data, and then re-formulating it as an ML problem. It requires openness, creativity, and an eagerness to learn new methodologies and explore new terrains.

We approach these problems as a team where great leaders are essential, meaning that you will have to be able to clearly explain your reasoning and code to engage the team as well as clients, investors, and others.

Responsibilities

  • Helping the team to succeed as a technical mentor

  • Challenge and inspire the team in state-of-the-art applied ML

  • Analyzing and planning problems, solutions, and delivery

  • Preprocessing, feature engineering, and dataset creation

  • ML model development

  • Validation of results

  • Data pipelining and infrastructure development

Our Stack

  • Python – standard open-source libraries

  • Scikit-learn and various specialized Python and R ML libraries

  • Cloud platforms such as GCP, AWS, Azure

  • Relational database management systems

  • Distributed processing such as Apache Spark

Background & Skills

  • MSc or Ph.D. in a quantitative field

  • 5 years of experience in leading end-to-end ML projects

  • Experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

  • Experience with software development in one or more programming languages, and with data structures/algorithms.

  • Excellent communicator (IRL, blog, events, etc)

  • Excellent understanding of a broad set of ML algorithms and frameworks

  • A passion for lean, clean, and maintainable code

  • The desire to grow and to share insights with others

Helpful knowledge & Experience

  • Leadership in fast-growing organisations

  • Product experience from idea to MVP and monetization.

  • Deep learning frameworks and theory

  • Data pipelining and infrastructure

  • DevOps experience, CI/CD, Kubernetes

  • 2 years of experience with state-of-the-art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts

NOTE : To Apply we require a work VISA for Sweden . Currently, we do not offer sponsorships.

Due to the summer holiday period, applications will be reviewed starting 27th of July.

About Team Modulai

At Modulai, we focus 100% on solving problems with machine learning (ML). We work in teams on a project basis, for clients, as part of the core team in startups where we have long-term engagements, and we also build our own ML products.

Learning and teamwork are central to how we work. Everyone in the team is or will soon be a full-stack ML engineer capable of scoping and developing end-to-end ML solutions. You should be able to do end-to-end machine learning products by yourself but never do it because we always work in teams. If there is data, we will do ML on it!

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