Principal Machine Learning Engineer

Key Skills

Aartificial intelligence, AWS, Docker, Effective communication skills, Git, Java Programming, Leadership Skill, Machine learning techniques, Python Programming

Job Description

The Principal Machine Learning Engineer at Workiva spearheads the strategic vision for advancing Machine Learning (ML) solutions, ensuring inclusivity, and fostering innovation. This role champions MLOps excellence, crafting cloud infrastructure solutions to expedite ML development cycles. As a technical leader, the Principal ML Engineer guides a team in creating Workiva’s Generative AI platform, empowering product teams to pioneer transformative solutions. They offer expertise and mentorship within the Machine Learning domain, driving progress through collaboration and technical acumen.

What You’ll Do

• Shape the strategic engineering roadmap for ML advancement

• Explore cutting-edge technologies for strategic ML endeavors

• Architect systems to facilitate agile ML development with robust observability

• Advise Workiva leadership and stakeholders on ML strategies

• Lead the team in adopting new ML technologies and methodologies to align with Workiva's goals

• Mentor junior ML engineers and scientists while establishing team standards and processes

• Collaborate with product teams to integrate ML solutions via APIs

• Maintain and evolve machine learning infrastructure

• Forecast and coordinate development timelines, ensuring timely deliverables

• Manage backlog and resolve interdependencies with other product teams

• Host ML models for product teams, ensuring monitoring and performance

• Develop automated tests tailored for ML solutions

• Troubleshoot and debug components across diverse service and application contexts

• Assist support teams in resolving production issues and participate in on-call rotations

• Conduct code reviews and engage external stakeholders in ensuring solution quality

• Communicate complex technical issues effectively to diverse audiences

• Collaborate with software architects, data experts, and product managers to design comprehensive software products meeting varied customer needs

What You’ll Need

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, or equivalent experience

• Minimum 8 years of ML engineering experience

Preferred Qualifications

• Technical leadership experience within an ML team

• Proficiency in architecting highly-available MLOps and ML solutions

• Expertise in the ML development cycle and associated tool sets

• Demonstrated ability to identify and apply ML solutions effectively

• Experience with Generative AI and integrating Gen AI models

• Strong organizational skills with the ability to manage multiple projects

• Proven track record of problem-solving and decision-making in ambiguous situations

• Proficiency in Python, GO, Java, or similar languages

• Familiarity with Git or equivalent version control systems

• Experience with Docker, preferably Kubernetes, and cloud platforms like AWS

• Agile/Sprint working environment experience

• Solid foundation in computer science principles and cloud-based distributed systems

• Excellent communication skills, both verbal and written, for effective collaboration

• Proficiency in debugging complex systems and knowledge of web protocols

• Understanding of commercial databases and systems performance tuning

• Experience in production-level testing and optimizing models for scalability

Working Conditions

• Minimal (<10%) travel for customer research or collaboration

• Reliable internet access for remote working opportunities

How You’ll Be Rewarded

• Salary range in the US: $167,000.00 - $284,000.00
• A discretionary bonus typically paid annually

• Restricted Stock Units granted at time of hire

• 401(k) match and comprehensive employee benefits package

The salary range represents the low and high end of the salary range for this job in the US. Minimums and maximums may vary based on location. The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience and other relevant factors.