Open Positions

ML Engineering Intern

Part- or Full-time roles available

Do you love to work at the absolute cutting edge? Do you want to help make renewables more sustainable, and work in a rapidly-developing field? If you want to join a team where you can have a real impact on the development of the business, integrate your passion for advanced computing, and work in a dynamic environment, we want to hear from you!

About ForeQast 

At ForeQast we apply advanced classical and quantum computing to solve the world’s toughest challenges. We are dedicated to building simulations of complex real-world systems that allow our customers to continually benefit from advances in both quantum and classical hardware. Our top priority is to build optimization tools on hybrid quantum-classical systems for industrials: whether that’s to improve production for wind farms or streamline supply chains.

About the position:

ForeQast is seeking outstanding Machine Learning Engineers for at least 4- to 6-month paid positions (starting from May 2021) for both part- and full-time employment. You can be either a student or a professional, and we’ll pay you appropriately. You will be one of the key engineers and developers for projects that focus on building and testing quantum algorithms for combinatorial optimization and machine learning. You will work with quantum algorithm specialists to productize their applied research, and business people to bring use cases to production-ready code.

The goal of the collaboration is the development of an optimization and forecasting platform for our industrial clients, as well as publishing in high-impact journals. This project offers you an opportunity for getting hands-on experience in top-level industrially relevant research and development.

Key roles include:

  • Work with researchers to take a core algorithm and scaling it to production
  • Design and build machine learning and deep learning solutions to new problems.
  • Provide technical feedback to support the team in how things should be built and deployed
  • Mentor other engineers to scale our machine learning team in terms of knowledge, technology and process. 
  • Advocate and implement the best practices to ensure the team is working effectively to deliver customer value while also constantly learning to remain up to date in a rapidly changing domain. 
  • Collaborate effectively with product management, engineering, design and other stakeholders to deliver products that have a quantifiable impact on the business.

Knowledge, skills, and abilities:

  • A degree in a related field (Data Science, Computer Science, Statistics or a quantitative-related field) or equivalent professional experience. 
  • Proven track record overseeing machine learning projects at all stages, from initial conception to implementation and optimization 
  • Hands-on experience building and deploying cloud applications either on AWS or Azure
  • Experience in developing and shipping machine learning, natural language processing or deep learning models.
  • Experience in Python and deep learning libraries, such as PyTorch or TensorFlow
  • Knowledge of DevOps (CI/CD, container orchestration, logging and monitoring, scripting)
  • Experience working with big data platforms (Hadoop, Spark, Hive) and orchestration frameworks (Airflow) and analytic environments (Databricks, Sagemaker)
  • Expertise with ML, DL and statistical modeling tools such as Pytorch, Tensorflow, Pandas, SciKitLearn, pyspark (in IDEs like PyCharm, Jupyter, etc.) 
  • Strong communication skills and can explain complex topics to both a technical or non-technical audience

ForeQast is an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

  • Seniority Level: Entry-level
  • Industry: Computer software
  • Employment Type: Paid internship (salary is competitive) 
  • Job Functions: Software development, Information Technology