Frontier Data/ML Engineer, Kili
€50-60k
Training data platform for enterprise AI
Open for applications
Training data platform for enterprise AI
21-100 employees
Open for applications
€50-60k
21-100 employees
Company mission
To help enterprise customers turn raw data into training data so that they can scale AI applications on their internal challenges to solve their issues.
Role
Who you are
- This role requires a deep understanding of data science, a passion for solving complex problems, and the ability to thrive in a collaborative, fast-paced environment
- Passion for Data Analysis: You enjoy diving into data, identifying patterns, and informing data collection paradigms to enhance model performance
- Problem-Solving Enthusiast: You are passionate about the challenges we face and are eager to contribute to applied machine learning and data science solutions
- Team Player: You are excited to join a small, dynamic team and be a key contributor to the company’s success
- You have at least 2-3 years of experience in a consulting firm specializing in data, AI, digital transformation, or a fast-paced company in a customer-facing role
- You possess a strong foundation in data science and machine learning, with knowledge of Python being mandatory
- You leverage data to drive decision-making and solve complex problems
- You love analyzing data :You'll inform data collection paradigms that improve model performance
- You care about the problems we’re trying to solve :You're interested in applied machine learning and data science for frontier AI
- You’re excited about joining a small team : As an early team member you’ll be a key part of advancing objectives across the company
What the job involves
- As a Data/ML Engineer, you will play a critical role in analyzing data, optimizing data collection pipelines, and ensuring the quality and diversity of data used to train machine learning models
- Your work will directly impact the performance and reliability of frontier LLMs, and you will be integral in advancing our objectives across the company
- Optimize Data Collection Pipelines: Enhance the efficiency and effectiveness of our data collection processes to improve model performance
- Implement Quality Control Processes: Develop and refine quality control measures to ensure diverse and unbiased data for robust model training
- Analyze Existing Data: Investigate current data sets to identify pain points and iterate on machine learning workflows
- Design Data Collection Workflows: Create workflows tailored to specific model requirements to maximize data quality and relevance
- Reduce Bias and Ensure Safety: Implement strategies to minimize biases and ensure the safety and reliability of our models
- Run Experiments: Conduct experiments to refine and improve leading models, making them more insightful, compelling, safe, and aligned with human values
Our take
According to digital business research, artificial intelligence is one of the primary areas of exploration for businesses looking to take advantage of growing tech developments. However, as Kili rightly emphasise, the efficacy and reliability of AI depends largely on the quality of data used to train it. Good data requires good labelling and the ability to store it for future use, and Kili is hoping to furnish enterprise businesses with precisely that.
The data labelling market is fiercely competitive, but the relatively young Kili has made impressive strides in their native France already. A roster of high profile clients including BNP Paribas, Safran, and Louis Vuitton demonstrates their ability to net quality enterprise custom across a breadth of markets using disparate data types. Plus, it has cemented them a strong reputation from which they can springboard in their planned expansion to the US market.
Here is where things may get tricky. The United States takes 40% of the global AI market, and has attracted a correspondingly strong crowd of competing startups and enterprise businesses. Kili will benefit from a reputable track record, but will have to play fierce to get ahead in the market.
Steph
Company Specialist at Welcome to the Jungle
Insights
Some candidates hear
back within 2 weeks
-16% employee growth in 12 months
Company
Funding (2 rounds)
Jul 2021
$25m
SERIES A
Jan 2021
$6m
EARLY VC
Total funding: $31m
Company benefits
- The opportunity to work in an international and friendly work environment
- The opportunity to build an expertise on THE topic that will be the future: AI
- The opportunity to join one of the most exciting startups of the French Tech as it is taking off
- Flexible work organization and access to remote work
- Afterworks every Thursday and 2 off-sites per year
Company values
- Ambition - Keeps us climbing the highest peaks
- Curiosity - Keeps us trying and discovering things anew
- Care - Keeps us focused on what matters most: people
- Honesty - Keeps us from going in circles
- Collaboration - Keeps us in the winning team
Company HQ
Saint-Philippe-du-Roule, Paris, France
Articles
Leadership
Previously co-founded the car-as-a-service company Tripndrive, then went on to freelance for The Boston Consulting Group.
Edouard d'Archimbaud
(CTO)Previously a Managing Partner at Creative Research Ltd., who then worked as a Data Scientist at Capgemini Consulting, and moved on to become Head of the Data and AI Lab at BNP Paribas.
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