ML Researcher (remote)

Fundamental

Europe +1 more

Remote

Who this role is best for

Best suited to ML researchers with strong software engineering fundamentals and expertise in structured data working in enterprise AI.

Best fit for

Candidates with strong software engineering skills and experience in structured data or foundation models
— “Strong fundamentals of software engineering”
Individuals who have worked on full lifecycle AI model development and distributed training
— “Full lifecycle of AI model development”
Researchers with a track record in building and deploying novel ML methods in enterprise settings
— “build technology that transforms how the world's largest companies make decisions”

Things to consider

Relocation support is available for those moving to office locations, but remote work is the primary arrangement
— “Relocation support for employees moving to join the team in one of our office locations”
The role is mission-driven and requires a cultural fit with low-ego and bias toward action
— “mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action”

How to stand out

Highlight your experience with distributed training and model scaling in your resume and interviews
— “Scaling up models and training regimes”
Emphasize your ability to contribute to end-to-end ML model development and evaluation
— “entire lifecycle of the models”
Showcase your work with classical ML and deep learning techniques in your portfolio or projects
— “Classical ML methods and algorithms”

Pace · Fast PacedCollaboration · MediumAutonomy · MediumDecision Impact · Company

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

Developed and deployed machine learning models
Improved model performance through novel methods
Contributed to open-source projects

Typical background

PhD or MSc in computer science/machine learningExperience in full lifecycle of AI model development

Skills & requirements

Required

Machine LearningSoftware EngineeringPythonAI Model DevelopmentML Infrastructure

Preferred

Published ResearchOpen Source ContributionsTabular Data Analysis

Stack & domain

PythonML FrameworksGpusTpusDistributed TrainingTabular ModelsTime Series ModelsRelational Foundation ModelsStructured DataLarge-scale Foundation Models

About the role

Original posting from Fundamental via Ashby

ABOUT FUNDAMENTAL
Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is giving businesses the Power to Predict.
At Fundamental, you'll work on unprecedented technical challenges in foundation model development and build technology that transforms how the world's largest companies make decisions. This is your opportunity to be part of a category-defining company from the ground-up. Join the team defining the future of enterprise AI.
KEY RESPONSIBILITIES
As part of the Research team, you will contribute to ideation, designing, implementing, and evaluating breakthrough Machine Learning models that will be deployed in the real world. Your work will be focused on the entire lifecycle of the models. Alongside the rest of the ML researchers in the team, you will be responsible for our models’ performance in every meaning of this word - whether this means achieving high evaluation scores through novel architectures and training methods, establishing the evaluation protocols themselves, or implementing methods that allow for efficient training and inference. The greatest research is done through solid engineering, so alongside the research you will also take part in ensuring that our research code allows swift, rapid development and testing of new ideas - both your own and the rest of the team’s.
MUST HAVE
Strong familiarity with the full research cycle in Machine Learning
Strong fundamentals of software engineering
Strong knowledge of Python, and its ML frameworks
Experience with:
Full lifecycle of AI model development
ML infrastructure frameworks and tools
Developing new ML methods, algorithms and models
GPUs (or TPUs) and distributed training
Scaling up models and training regimes
Knowledge of:
Classical ML methods and algorithms
Deep Learning techniques
Expertise in at least one of the following:
Tabular models, time series models, relational foundation models, structured data, large-scale foundation models
NICE TO HAVE
Published research at AI conferences
Contributions to open source ML projects
Experience working with tabular data / predictive analytics
High Kaggle rank
BSc/MSc/PhD in computer science/machine learning
BENEFITS
Competitive compensation with salary and equity
Comprehensive health coverage for you and your dependents
Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys
Relocation support for employees moving to join the team in one of our office locations
A mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action

Source: Fundamental careers (Ashby)