Tubular, Lineup and FatTail have partnered with Chartbeat to help you grow reach and revenue for your content.
Chartbeat’s (www.chartbeat.com) mission is to help content creators around the world better connect with their audiences.
In 2023, Chartbeat joined forces with Tubular, the leader in global social video intelligence and measurement, and Lineup Systems, the leading global provider of media sales technology. Together, we’re expanding the ecosystem of insights we provide to enterprise content creators who are developing audiences and revenue streams across channels. We now serve more than 1,000 brands globally, including The New York Times, the BBC, ESPN, Gannett, Vox, BuzzFeed, Paramount, WB, Mediahuis, Hearst, McClatchy, and GQ.
You'll be joining a diverse group of focused, hard-working people who are passionate about doing work that's challenging and fun—and who strive to maintain a healthy work/life balance.
The Team
We are passionate about large-scale data systems, leveraging best-in-class technologies such as Snowflake, Kubernetes, Kafka, and Python. Machine Learning is a vital part of Chartbeat’s Data Engineering organization, focused on curating, enriching, and modeling data to power intelligent features, drive product discovery, and apply Large Language Models (LLMs) to real-world applications and strategic business decisions.
Responsibilities
As a Machine Learning Engineer at Chartbeat, you will:
- Contribute the design, development, and deployment of machine learning systems at scale
- Collaborate with product managers and cross-functional engineering teams to deliver ML and generative AI powered features
- Explore, Experiment and Prototype upcoming generative AI technologies to solve complex business challenges
- Help define and drive the technical roadmap for ML and AI initiatives
- Deploy with the help of cross functional Eng and Infra teams to build and deploy models that are explainable, maintainable, and effectively monitored in production
- Contribute to architectural decisions and long-term planning for ML infrastructure
- Participate in the engineering on-call rotation to maintain the reliability and performance of production systems
About You
- 5+ years working as a machine learning engineer, ideally within a B2B SaaS environment
- Experience in training, building, and deploying machine learning models in production is essential
- Strong Python proficiency
- Proficiency with advanced SQL querying and knowledge of common data warehouse environments, such as Snowflake, or similar
- Experience working with modern ML and data tooling (Such as PyTorch, TensorFlow, Spark and MLFlow)
- Experience implementing and scaling LLMs or foundation models in production environments is a plus
- Experience working with data pipelines that support multi tenant usages with different databases (such as Snowflake/BigQuery etc) for large, high-scale applications
- Theoretical knowledge of statistical and machine learning algorithms, as evidenced by an undergraduate or graduate degree in a mathematics, computer science, or engineering-related discipline
Compensation and Benefits:
We are proud to offer our team members a competitive compensation plan that includes:
- Comprehensive Health, Dental, and Vision Insurance
- 401K with company match (100% of the first 3% and 50% of the next 2%)
- Fully Paid Parental Leave - 18 weeks for birthing parents, 12 weeks for non-birthing parents
- Phone and internet stipend
- Wellness, learning, and coworking reimbursements
- Flexible work hours
- Unlimited PTO
- 11 paid holidays and December holiday closure
- Company-wide outings
- The compensation range for this position is $170,000 - $185,000
Top Skills
Similar Jobs
What you need to know about the Colorado Tech Scene
Key Facts About Colorado Tech
- Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
- Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
- Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
- Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute
