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Machine Learning Scientist
Bring ingenuity and knowledge to supply chains: solve for crucial issues in the world’s most challenging environments.

Macro-Eyes is recruiting Machine Learning Scientists. Build cutting-edge ML powering new solutions for supply and demand networks: save lives; put a dent in climate change; enable organizations to do more with less; have a purpose.

This role will involve a combination of ML design+build for our core product (creating generalizable, scalable ML libraries) and deployment specific work, designing how our technology engages customer needs in the real world.

Join a diverse, talented, international machine learning team led by renowned MIT professor Suvrit Sra. We constantly face challenges beyond the limits of known ML theory and models. You will co-build the first predictive supply chain for global industry and for healthcare. We have been busy on new, deployed systems for understanding demand for lifesaving care; computer vision apps to automate ground-truth data collection, ranging from efficient ML that must run entirely on-device to deep learning systems working on satellite imagery; representation learning models that help us understand context; delivering geolocation accurate to within 25m in locations without GPS.

You will create transformative solutions the world has never seen - cutting-edge ML that forms products, deployed globally to make supply chains capable of anticipating risk and opportunity. Real science and real-world impact. Lives lie in the balance.

The ideal candidate will be excited by the fact that the work we will do together is largely without precedent.

Strong drive to use ML for social good is the most important characteristic you will bring. Complement that with a solid background in ML, good knowledge of statistics, along with experience in working with real-world messy data as a plus. Desire to publish is a plus. Python fluency needed. Demonstrated ability to make meaningful contributions to projects with an open-ended research flavor is valuable.

Requirements

  • Happy to dive into data
  • Desire to continually learn and grow; overall open-mindedness
  • Experience building models based on empirical observations; understand strengths and limitations of this approach.
  • Experience working with diverse data types including images, structured data, and natural language.
  • Experience programming in Python, and one additional language (R, C, C++, Java).
  • Aware of current best practices in machine learning.
  • Knowledge of statistics, including hypothesis testing with parametric and non-parametric tests and basic probability.
  • ‘Small data’ experience: demonstrated ability to develop and implement creative approaches for making ML work with limited data.

Warning: the work will not be like doing data science at Big Tech: the data will not be pristine and what’s required for success is much harder than throwing a series of models at the problem. We expect you to think in terms of hypotheses and experimental design like a scientist and have the drive and ingenuity of a hacker. Even better if you have hands on experience building predictive models.

We’re excited if you have expertise in one of these areas:

  • Computer vision
  • Reinforcement learning
  • Forecasting (time series)
  • Optimization, operations research
  • NLP
  • Synthetic data, generative models
  • Remote sensing
  • Graph theory / Graph neural networks

We believe fierce drive and motivation is more valuable than specific skill – critical thinking and willingness to learn on the job goes far when confronting new challenges.

What we ask of you:

  • Take a task and run with it - there’s little hierarchy.
  • Speak up. We all learn every day. Ask for help, say when you need something.
  • Be forthcoming and accountable. About time, scope, questions, needs, options, deliverables.
  • Be nice to work with. Nothing extraordinary was ever achieved alone.
  • Exercise a product frame of mind: persistent drive to design and build structures that are repeatable and scalable.
  • Demo, Deploy, Repeat. Think about how to show, rather than tell, what you’re building. Engage with real systems and real world data as soon as possible to validate and learn.

What you need to bring:

  • Comfortable working with small groups in a fast-paced environment with cross-functional teams.
  • Energy, boundless curiosity, open-mindedness.
  • Willingness to learn and grow.
  • Respect for audiences and users. Willingness to step into someone else’s shoes (and the imagination to do so).

Why you want to work with us:

  • Real-world impact and state-of-the-art science. We develop technology and products that save lives and help organizations do more with fewer resources. We break new ground in AI by creating systems that learn from sparse, complex data in some of the most challenging environments on earth.
  • We are growing rapidly and engaged in many of the most important issues of our time. Being part of MACRO-EYES is rich in opportunity.
  • Competitive compensation and generous benefits.
  • Work with an amazing group of people.
  • Work from almost anywhere. This role will be remote with the freedom to choose how and when to work. Our remote team was executing at the highest level before COVID-19, and we expect to continue to be a largely remote, high-performing team. In continuation of defying the trend, we will open our first physical offices in 2022. You will have access to your choice of hardware and a travel budget to interact with the distributed team in person.

Macro-Eyes is dedicated to building an inclusive workforce where diversity is valued. Macro-Eyes is an equal opportunity employer. Every qualified applicant will be considered for employment. Macro-Eyes does not discriminate based on race, color, religion, gender, gender identity or orientation, genetic information, age, national origin, marital status, disability status, political ideology, military or protected veteran status, or any other characteristic protected by applicable federal, state, or local law.

We apologize: Due to large volumes of applicants, only shortlisted candidates will be contacted.