Senior Machine Learning Engineer

Who we are 

Stensul dramatically reduces marketing content creation time - by up to 90% - so teams can better focus on improving marketing performance. Stensul makes this possible by streamlining the collaboration process and simplifying marketing asset creation for all marketers so they can create high-performing campaigns that drive stronger results. Stensul integrates with all leading ESPs/MAPs, workflow platforms, image digital asset management platforms, live content, link tracking, and messaging platforms. Top brands that trust Stensul to solve their most demanding marketing creation problems include BlackRock, Cisco, Demandbase, Equifax, Greenhouse, Siemens, and Thomson Reuters.

At Stensul, our top priority is maintaining a people-first, diverse, and inclusive culture. We look for people that live by our core values - Garra, Learning Agile, Above & Beyond, and Team Players. We’re committed to investing in your growth through mentorship, coaching, and meaningful professional development. If you thrive in a fast-paced environment and are eager to take ownership of a large, revenue-generating area of the business, we want to hear from you!

Mission

The Machine Learning Engineer will design and implement the company’s first Generative AI (GenAI) and Machine Learning application, creating a foundation for scalable, efficient, and cost-effective AI/ML solutions. This role will also play a critical role in mentoring, promoting best practices in coding and data modeling, and supporting the integration of GenAI productivity gains across the company.

Outcomes

1. Develop a GenAI/ML Application: Successfully build and deploy the first GenAI or ML application integrated into the company's product within the first 6 months.
2. Establish Scalable ML Architecture: Set up a robust, reusable platform for GenAI and ML, compatible with existing infrastructure and suitable for adoption by other teams by the end of the first year.
3. Mentor and Upskill Data Engineer: Elevate the coding, data modeling, and best practices knowledge of the Ssr Data Engineer to foster a collaborative, high-standard team environment.
4. Optimize Costs and Efficiency: Implement solutions that balance performance and cost-effectiveness across GCP, Databricks, and BigQuery ecosystems.
5. Cross-functional Collaboration: Partner effectively with data, product, and engineering teams to ensure seamless integration and high impact of ML solutions across the company.

 

Competencies

• Technical Generalist in ML Engineering: Demonstrates a strong command of machine learning concepts, model development, and deployment on cloud platforms, particularly in cost-aware and scalable ways.
• Experience with GCP, Databricks, BigQuery, MongoDB, and MySQL: Proficient with the company’s core technologies, ensuring solutions are efficiently implemented and maintained across these systems.
• Mentorship and Communication Skills: Able to mentor junior team members and communicate complex technical concepts to non-technical stakeholders.
• Strong Data Modeling and Transformation: Skilled in transforming raw data into structured, optimized formats for analytics and ML purposes.
• Resource Optimization and Cross-System Integration: Experienced in designing solutions that maximize performance while considering budget and cross-vendor compatibility.
• Autonomous: Strong management of competing priorities, negotiation and prioritization based on deep understanding of the different stakeholders needs

 

Primary Responsibilities

• Build and Deploy GenAI/ML Applications: Develop, train, deploy and monitor the company’s first GenAI &/or ML solution integrated into the product.
• Architect and Scale ML/AI Platform: Design a scalable platform for GenAI and ML use cases to allow further expansion into additional product and team needs, using Databricks, BigQuery, and relevant GCP services.
• Mentorship: Provide guidance on coding standards, data modeling, and best practices, helping elevate the technical skills and independence of the engineer and data team.
• Optimize ML Resource Usage: Build solutions with an awareness of budget constraints, selecting the most cost-effective and performant configurations and tools.

Cross-functional Collaboration and Communication: Engage with teams to align on project requirements, share insights on ML feasibility, and ensure smooth integration of ML solutions within the product ecosystem.

Stensul is proud to be an Equal Opportunity Employer building a diverse and inclusive workforce.

If your experience is close (even if not a perfect fit) to what we’re looking for, please consider applying. Experience comes in many forms – skills are transferable, and passion goes a long way. We know that diversity makes for the best problem-solving and creative thinking, which is why we’re dedicated to adding new perspectives to the team and encourage everyone to apply.