Software Engineering (regular)
Appsilon is an ambitious and fast-growing software house and consultancy specializing in decision support systems and machine learning with Fortune 500 clients across the globe.
We are a unique company driven by a mission to improve our society and environment. Some examples of our #data4good work include contributing to wildlife preservation in the National Parks of Gabon, building COVID-19 dashboards, and improving data science tools for Doctors Without Borders.
In the machine learning space we specialize in computer vision, applying it in cases making impact on biodiversity and researching new approaches.
We are also a global leader in R and Shiny , which are used by companies of all sizes to build analytical applications. When companies run into difficult problems or want to initiate large-scale enterprise projects, they come to Appsilon.
Before you apply, please read our code of conduct .
Every few months we start completely new projects and dive into a completely new world. One day we identify species of monkeys lurking behind trees of a rainforest, another day we analyze satellite images to help mitigate natural disasters, and then dive into the arctic ocean, helping researchers understand the changes in those ecosystems.
Our projects are not only an opportunity to test our skills in difficult statistical, algorithmic, and technological problems but also an opportunity to learn about different research fields and, most importantly, contribute to their advancement.
Some examples of our past projects :
Open source wildlife detection app , built for usage in remote areas
Analysis of damage after natural disasters based on satellite images : https : / / demo.appsilon.ai / apps / building damage assessment
We took 5 / 811 place in the Hakuna Ma-data competition
A research paper applying machine learning in ecological modelling
Your Role as a Machine Learning Engineer
Regular duties will include :
Collecting, curating, possibly preprocessing a dataset
EDA - exploratory data analysis
Understanding and visualizing statistical properties and peculiarities of the dataset
Running and monitoring model’s training
Investigating the model’s performance, identifying strong and weak points
Pipeline setup and improvements
Making sure the process above is modular and reproducible
Handling meetings with the client / partner
Sharing results, challenging assumptions, understanding the role of ML in their workflow
Helpful skills and experience
Great Software Engineering background
Extensive Python knowledge
Experience with PyTorch or Tensorflow
Experience in data wrangling
Experience with machine learning pipelines and experiment reproducibility
Trained analytical thinker
Able to switch between hacker mentality of getting things to work and organized engineer adhering to basic principles when refactoring or building key pipeline elements
Able to abstract from technical issues and communicate also on high level
At least B2 level of English
What’s in it for you?
Salary 10000 - 16000 PLN + VAT on B2B contract
26 days of paid holidays + an equivalent of public holidays in Poland, est. 11 days in 2021
5% of salary in Professional Development Budget to spend on activities that help you grow
33 days (paid 80%) per year on B2B when on a sick leave
Remote work with flexible working hours adjusted to your time zone and family life.
4 paid days per year to be used for training / conferences, events, or workshops for your professional development
Private health care insurance (in Poland) (Polmed)
FitProfit or FitSport membership card (in Poland)
AskHenry a personal assistant works great in large Polish cities, elsewhere limited to online support
Projects that have a real impact on the world. More on https : / / appsilon.com / data-for-good /
Technologies and tools you will be using
All sorts of Pythonic tools for
Data processing - pandas, numpy, Pillow, opencv,
Data visualisation - matplotlib, seaborn, plotly,
Modelling - PyTorch (and fast.ai), TensorFlow (and Keras), scipy,
Experiment tracking - Weights&Biases, Neptune, Domino, Tensorboard,
Dashboards - rather limited, but still maybe sometimes - streamlit, django, starlette, ...
Occasionally R world
Mostly to interface with Shiny dashboards
Possibly if data source / preprocessing was implemented in R by a client
Code tools (GitHub / GitLab, your favourite IDE, sometimes RStudio)
General tools (bash / shell)
Internal tools (Slack, Outline, Clickup, G Suite)
What can you expect during the recruitment process?
Screening call with Talent Manager
Interview with the ML Team
Conversation with the CTO