Machine Learning Evangelist

Key Skills

Docker, HuggingFace, Machine learning techniques, Pandas, Presentation skills, Python Programming, PyTorch, Writing

Job Description

The power of prediction starts with HumanSignal. We build software that helps people do what only they can do – give meaning

Data fills our modern world. It flows prolifically inside organizations, customer interactions, through product usage, environmental research, healthcare imaging, and beyond. What if we could use any of that historical data to predict the future? The fact is, in most cases we can make predictions through Machine Learning and AI, but to do so in a meaningful and impactful way, historical data needs to be accurate, comprehensive, and absent of bias.

We believe that data labeling is a team sport. Data scientists, subject matter experts, engineers, operations, and annotators must work collaboratively to ensure quality results and an efficient process. That’s why we created Label Studio, which has quickly become the most popular open source data labeling platform with a 250K+ users around the world and millions of labeled samples, alongside a community of thousands of data scientists sharing knowledge and working to advance data-centric AI.

About the Opportunity:

HumanSignal is seeking an experienced Machine Learning Evangelist to support the growth of Label Studio, the most popular OSS data labeling platform with 14,000+ GitHub stars and more than 10,000 community members in slack. As a ML Evangelist you will educate and inspire current and future Label Studio users in the importance of data quality and integrity for Machine Learning Workflows. The rapid adoption of LLMs and foundation models has lit up our space, and we’re looking for an expert to help machine learning engineers and data scientists apply data-centric AI to the new generative AI workflows and stack. You’ll have the opportunity to make a big impact in an engaging problem space by influencing product strategy, technical content creation, speaking, engaging with users in our community channels, integrations engineering, and social media engagement.

Can you debate LLM fine tuning techniques, do you have a passion for open source, can you about agents and transformers at network events? If this sounds like you, we want to hear from you!

What You'll Bring:

• A technical or scientific background in Machine Learning and Data Science, with a demonstrable ability to use industry-standard tools like Python, PyTorch, Pandas, Docker, Hugging Face and other critical toolchains in the machine learning and data science workflow.
• Experience and enthusiasm for written and spoken technical communication with the ability to convey technical insights to both non-technical and technical audiences.
• Empathy for engineers of any level.
• Self-motivated and effective working in a remote first environment.
• A collaborative spirit, with excellent interpersonal skills.
• 4+ years of relevant work experience

Within your first month:

• Get up to speed with the Label Studio platform and related ecosystem.
• Research, learn, and cover the latest research topics in ML and how they relate to the Label Studio Community.
• Collaborate with the internal team on communication, product launches, and product strategy.

At 3 months:

• Drive conversations and engagement with our community members, help them learn new things and excel at their projects.
• Create technical content: blog posts, webinars, videos, guides, white papers, and conference talks.
• Build relationships with adjacent machine learning and data science communities.

At 6 months:

• Continue to actively develop and share your own knowledge through:
• speaking at relevant conferences and events,
• writing regular content for the Label Studio blog and notable industry websites,
• collaborating with partner communities in developing novel integrations.
• Work cross-functionally within HumanSignal to drive company-wide growth metrics.

At 1 year:

• Have demonstrated a command of the broader machine learning ecosystem and Label Studio’s place in it, with a strong portfolio of writing, presentations, and community engagement that has helped to solidify Label Studio's position as the most popular open-source data annotation platform.
• Advise company leaders on emerging trends and strategies to build upon them.

It is an exciting time at HumanSignal, we are a growing startup and at this stage we are constantly evolving. While we have put a lot of thought into your first and most important initiatives, it’s only an example and something we will work on together. We're always learning and growing, so like us this role will evolve and expand. We hope that this opportunity sounds exciting to you and that you consider joining us on our journey!

At HumanSignal

HumanSignal is an Equal Opportunity Employer. We are committed to building an organization that welcomes diverse backgrounds and lifestyles. Our goal is to create an inclusive work environment that is equitable and where everyone feels they belong. We foster open and transparent communication and a workplace where discrimination and harassment are not tolerated. If you need any accommodations or assistance to make your interviews more accessible please do not hesitate to reach out to us.

We do not discriminate against employees or applicants based on gender identity or expression, sexual orientation, religion, age, race, military/veteran status, citizenship, pregnancy status, or any other differences.

At HumanSignal we pay based on regional compensation market rate ranges across the globe. We are hiring for this role across North and South America. For US based candidates in Colorado and most US remote metro areas our base cash compensation for this role is targeted at $155,000 to $185,000 USD. For candidates in San Francisco and New York metro areas the base cash compensation range is $175,00 to $195,000 USD. These ranges are provided by market data and are in good faith. The final offer details are determined by several factors including candidate experience, expertise, as well as applicable industry knowledge and may vary from the pay ranges listed above.