Careers

Software Engineer

Experience level: 1-4 years of software engineering experience

Location: Anywhere in Portugal. We encourage team members to work both on-site and remotely, as they see fit. We provide access to on-site offices in Porto and Lisbon.

What we offer:

• Salary + bonus (adjusted according to experience and achievements)
• Flexible and fast-paced environment
• 35-hour work week
• 25 days of Holidays
• Personal development tools: access to books, publications, mentors
• Team retreats in very cool locations
• Other agreed tailored benefits to your needs

Job Description: As a Software Engineer, you will be responsible for developing and maintaining our simulation API. You will work closely with our team of researchers to implement cutting-edge algorithms and techniques for simulating physical systems. You will also work with our clients to understand their simulation needs and develop custom solutions to meet their requirements.

Responsibilities:

• Design and develop our simulation API using modern programming languages and tools
• Work closely with our research team to understand the latest advances in simulation technology
• Collaborate with our clients to understand their simulation needs and develop custom solutions
• Write clear, concise, and well-documented code
• Perform unit testing and integration testing to ensure the quality of the software

Requirements:

• Bachelor’s or Master’s degree in Informatics Engineering, Computer Science, or a related field
• Strong programming skills in Python
• Knowledge of data structures and algorithms
• Experience with software development in a Linux environment
• Experience with software development best practices, including version control, unit testing, and continuous integration
• Excellent problem-solving and analytical skills
• Good communication and collaboration skills
• Ability to work independently and as part of a team
• Experience with cloud computing and/or containerization (e.g., Docker, Kubernetes)

Nice-to-have:

• Experience with Machine Learning and/or data analytics
• Knowledge of simulation techniques and algorithms for physical systems (e.g., fluid dynamics, molecular dynamics)
• Experience with high-performance computing (HPC) environments

What will help you to do your job well:

• People person - You love to interact and build relations with people, you really care deeply about them and you will do your best to make them successful.
• Strategic thinker - You always try to connect the dots to see the big picture. You are great at prioritizing the high impact things. When you execute, you make a plan first, but you are flexible to change it according to new information.
• Love of Learning - Learning new things brings you excitement and fulfillment.
• Trusted advisor - You’re a great listener, confident and advisor at the same time.

Other things that will improve your application:

• Public GitHub repositories with your own projects
• Technical blog posts, tutorials or recorded talks

Deep Learning Engineer

Experience level: 4+ years of ML research or engineering (PhD is a strong plus)

Location: Anywhere in Portugal. We encourage team members to work both on-site and remotely, as they see fit. We provide access to on-site offices in Porto and Lisbon.

What we offer:

• Salary + bonus (adjusted according to experience and achievements)
• Flexible and fast-paced environment
• 35-hour work week
• 25 days of Holidays
• Personal development tools: access to books, publications, mentors
• Team retreats in very cool locations
• Other agreed tailored benefits to your needs

Fluency with:

• Deep Learning, with particular focus on sequence models (LSTMs, Transformers, etc) and Graph Neural Networks
• Training and fine-tuning large models
• One or more DL frameworks: Tensorflow, PyTorch or JAX
• Python, Git/GitHub

In addition, the following skills are valued, but not strictly required:

• Previous contact with ML tools and platforms to run experiments at scale (e.g. Ray)

The role:

You will be part of a team applying advanced Deep Learning techniques to fundamental problems in Molecular Biology. This can involve dealing with genetic or proteomic datasets, creating ML models that process sequential data about genes and proteins, model their 3d geometry as well as interactions they establish among themselves and with different molecules. You will make use of concepts from Geometric Deep Learning, such as Graph Neural Networks, and try to incorporate physical, geometric and evolutionary priors with other sources of experimental data, into an end-to-end learning architecture.

In this position, you will be spending a lot of your time modeling Biology problems from the perspective of Machine Learning. You will not be alone in this challenge. You will interact with people of different backgrounds, ranging from Computer Science, Mathematics, Physics, to Biology experts. The kind of work you will be doing involves a lot of ML exploration and experimentation. We will be trying to keep the cycle idea-implementation-experiment as agile as possible, by having clear metrics and good ML infrastructure and best-practices in software engineering.

What will help you to do your job well:

• People person - You love to interact and build relations with people, you really care deeply about them and you will do your best to make them successful.
• Strategic thinker - You always try to connect the dots to see the big picture. You are great at prioritizing the high impact things. When you execute, you make a plan first, but you are flexible to change it according to new information.
• Love of Learning - Learning new things brings you excitement and fulfillment.
• Trusted advisor - You’re a great listener, confident and advisor at the same time.

Other things that will improve your application:

• Relevant research articles where you were a co-author
• Public GitHub repositories with your own projects
• Technical blog posts, tutorials or recorded talks
• Certificates of online courses related with Deep Learning (e.g. from Coursera)

ML Ops Engineer

Experience level: 2+ years of ML Ops (4+ years of ML-related experience in total)

Location: Anywhere in Portugal. We encourage team members to work both on-site and remotely, as they see fit. We provide access to on-site offices in Porto and Lisbon.

What we offer:

• Salary + bonus (adjusted according to experience and achievements)
• Flexible and fast-paced environment
• 35-hour work week
• 25 days of Holidays
• Personal development tools: access to books, publications, mentors
• Team retreats in very cool locations
• Other agreed tailored benefits to your needs

Fluency with:

• Using one or more DL frameworks: Tensorflow, PyTorch or JAX
• Training models using hardware accelerators at scale: GPUs or TPUs
• Using Cloud computing infrastructure (Google Cloud, AWS, or Azure)
• Creating and managing Docker containers
• Implementing distributed systems for ML training and performing hyper-parameter optimization at scale (e.g. using frameworks as Ray)
• Building, maintaining and administering computational infra-structures composed of clusters of local machines (CPU+GPU)
• Building data pipelines for producing, managing and converting training data to/from various formats (e.g. json, tfrecords)

In addition to those, the following experience would be valued:

• Track record of supporting ML research teams
• Dealing with checkpointing and preemptible virtual machines (e.g. spot instances)
• Logging and visualizations frameworks (e.g. TensorBoard, Weights and Biases, Neptune or others)
• Experience in developing and maintaining ML Leaderboards (e.g. Kaggle-style)
• Deploying ML models to a cloud API

The role:

You will be part of a team of Deep Learning researchers and engineers, tackling fundamental problems in science. Your main goal will be to help accelerate the speed at which we conduct research and innovation. You are expected to build and develop tools and infrastructure that should have a multiplicative impact on the productivity of the entire team. This will involve building and supporting experiment infra-structures such that our ML models can train faster as we increase our computational resources, as well as building all the tooling such that the research we produce is reproducible (save hyper-parameters and config files used, experimental logs, model checkpoints, etc.). You will also be responsible for setting up or building the tools that enable us to do better tracking and management of experiments, including leaderboards and visualizations frameworks.

What will help you to do your job well:

• People person - You love to interact and build relations with people, you really care deeply about them and you will do your best to make them successful.
• Strategic thinker - You always try to connect the dots to see the big picture. You are great at prioritizing the high impact things. When you execute, you make a plan first, but you are flexible to change it according to new information.
• Love of Learning - Learning new things brings you excitement and fulfillment.
• Trusted advisor - You’re a great listener, confident and advisor at the same time.

Other things that will improve your application:

• Relevant research articles where you were a co-author
• Public GitHub repositories with your own projects
• Technical blog posts, tutorials or recorded talks
• Certificates of online courses related to ML, cloud computing, infrastructure (e.g. from Coursera)

Hardware Engineer

MEng in Electrical Engineering, Electronics or similar.

Engineering Experience: 4+ years

Location: Anywhere in Portugal.

In this role you will work in close collaboration with your colleagues to identify computational bottlenecks in various projects and propose FPGA-based solutions that other team members can use in a plug-and-play fashion. You will be contributing to the R&D infrastructure at Inductiva that will allow us to go from an idea of how to speed-up a certain computational stage, to prototype an accelerator, and finally to a production-level solution that we can deploy both internally and externally to our partners.

Your day-to-day might be quite diverse, and include things like educating the team on hardware design and architecture, or just spending some time reading papers and studying background material from Maths/Physics/ML to sharpen your skills and expand your knowledge.

To give you a flavour of the type of work you will be developing at Inductiva, here are some links to interesting resources and prior work by other groups:

• Ising Model Optimization Problems on a FPGA Accelerated Restricted Boltzmann Machine, arXiv:2008.04436
• Combinatorial optimization by simulating adiabatic bifurcations in nonlinear Hamiltonian systems, H. Goto et al., Science Advances 2019
• Finding a Hadamard Matrix by Simulated Annealing of Spin-Vectors, arXiv:1606.03815

Technologies:

• Programming languages: VHDL, Verilog and Python
• FPGAs: Xilinx
• Source control: Git
• Operating Systems: Linux / MacOS

Send us your CV & Cover Letter

Spontaneous Application

MSc or PhD in Mathematics, Physics, Computer Science or Engineering

Research Experience: early career

Location: Anywhere in Portugal.

We are currently not in need of filling up a “vacancy” for a specific role or project, but we are always looking for highly motivated candidates that have a solid scientific or engineering background. If you think you would be a great fit for Inductiva we invite you to start a conversation with us.

In this role, you will be applying Machine Learning methods to problems in Physics or Mathematics. This should provide you an opportunity to use your scientific background while developing your skills in Machine Learning and contributing to the broader research efforts of the entire team.

Your work may involve: designing algorithms that search for solutions in large combinatorial spaces using pattern recognition techniques and/or training neural networks capable of solving partial differential equations. There are many promising directions in this field that may benefit from Deep Learning methods.

To give you a flavour of the type of work we aim to develop at Inductiva, here are some links to interesting papers by other groups:

• Machine Learning for Combinatorial Optimization, arXiv:1811.06128
• A deep learning algorithm for solving partial differential equations, arXiv:1708.07469
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, M. Raissi et al., JCP 2019

You won’t need to be an expert in ML from the beginning, as you will be part of a team with ML researchers that will provide guidance, time and support for you to learn.

Send us your CV & Cover Letter