Kcura·42 minutes ago
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Staff AI Engineer
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What you'll do
- Define and evangelize the multi-year technical roadmap for ML Ops, aligning platform architecture with product and research objectives.
- Provide technical direction and mentorship across multiple engineering teams and organizations.
- Drive the design and architecture of training, inference, and monitoring systems focused on scalability, extensibility, performance, and reliability.
- Partner with product, program, and data science leaders to scope and deliver cross-functional roadmaps.
- Evaluate emerging ML Ops technologies and establish engineering standards and best practices.
- Champion advanced optimization techniques, including sparsity, quantization, and pruning, to improve performance and cost efficiency.
- Collaborate with security teams to maintain strong data protection practices and responsible AI standards.
- Lead architecture reviews, publish engineering best practices, and mentor senior and lead engineers across the organization.
What they're looking for
- 8+ years of professional software engineering experience.
- 5+ years of experience working in ML/AI or big data environments.
- 4+ years of technical leadership experience across multiple teams.
- Expert-level proficiency in Python, Java, or Scala for production systems.
- Deep hands-on experience with Docker, Kubernetes/Helm, and infrastructure-as-code tools such as Terraform or Pulumi.
- Proven experience building and operating CI/CD pipelines for machine learning workflows using technologies such as Prefect or Airflow.
- Experience deploying and operating secure, monitored services on AWS, Azure, or GCP.
- Demonstrated ability to mentor senior engineers, influence technical strategy, and drive cross-team initiatives to successful completion.
Nice to have
- Master’s or PhD in Computer Science, Engineering, Mathematics, or a related field.
- Recognized technical thought leadership through open-source contributions, conference presentations, or publications.
- Experience scaling ML platforms utilizing distributed data technologies such as Spark or Kafka.
- Expertise implementing advanced model optimization techniques, including compression, pruning, and quantization, in production environments.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
Posting Type
Hybrid/Remote
Job Overview
WHO WE ARERelativity is a leading legal data intelligence company building technology that helps users organize data, discover the truth, and act on it with confidence. Our AI-powered, cloud platform, RelativityOne, transforms massive volumes of complex information into actionable insights for litigation, investigations, regulatory inquiries, data breach responses, and other high-stakes legal work where accuracy and trust are crucial.
The world’s largest law firms, corporations, and government agencies rely on Relativity’s legal AI software to securely surface and manage the most relevant and impactful information in their matters. Beyond our commercial impact, we are committed to expanding access to technology and supporting pro bono legal work.
Job Description and Requirements
WHAT WE DO
The AI team at Relativity is focused on helping users discover the truth faster and act on data with confidence through AI-powered capabilities embedded throughout the discovery process.
- Build trusted AI solutions that improve user experiences, products, investigations, and legal matters.
- Develop world-class tools to solve complex challenges through experimentation and innovation.
- Leverage AI across all stages of the discovery lifecycle to improve outcomes and optimize product operations.
- Invest in modern data infrastructure, data pipelines, and data lake technologies that enable large-scale AI and analytics.
- Foster a culture of exploration, experimentation, continuous learning, and innovation.
ABOUT THE ROLE
Staff AI Engineers at Relativity operate as domain architects and strategic accelerators. This role works across multiple engineering and data science teams to define the long-term vision for Relativity’s machine learning platform, guide high-impact initiatives from concept through production, and ensure AI capabilities remain secure, reliable, scalable, and cost-effective.
WHAT YOU’LL DO
- Define and evangelize the multi-year technical roadmap for ML Ops, aligning platform architecture with product and research objectives.
- Provide technical direction and mentorship across multiple engineering teams and organizations.
- Drive the design and architecture of training, inference, and monitoring systems focused on scalability, extensibility, performance, and reliability.
- Partner with product, program, and data science leaders to scope and deliver cross-functional roadmaps.
- Evaluate emerging ML Ops technologies and establish engineering standards and best practices.
- Champion advanced optimization techniques, including sparsity, quantization, and pruning, to improve performance and cost efficiency.
- Collaborate with security teams to maintain strong data protection practices and responsible AI standards.
- Lead architecture reviews, publish engineering best practices, and mentor senior and lead engineers across the organization.
WHAT WE’RE LOOKING FOR
Required
- 8+ years of professional software engineering experience.
- 5+ years of experience working in ML/AI or big data environments.
- 4+ years of technical leadership experience across multiple teams.
- Expert-level proficiency in Python, Java, or Scala for production systems.
- Deep hands-on experience with Docker, Kubernetes/Helm, and infrastructure-as-code tools such as Terraform or Pulumi.
- Proven experience building and operating CI/CD pipelines for machine learning workflows using technologies such as Prefect or Airflow.
- Experience deploying and operating secure, monitored services on AWS, Azure, or GCP.
- Demonstrated ability to mentor senior engineers, influence technical strategy, and drive cross-team initiatives to successful completion.
Preferred
- Master’s or PhD in Computer Science, Engineering, Mathematics, or a related field.
- Recognized technical thought leadership through open-source contributions, conference presentations, or publications.
- Experience scaling ML platforms utilizing distributed data technologies such as Spark or Kafka.
- Expertise implementing advanced model optimization techniques, including compression, pruning, and quantization, in production environments.
WHY WE COULD BE A GREAT FIT
Impactful Mission
- Build systems that help customers organize data, discover the truth, and act on it in high-stakes legal matters.
Engineering at Scale
- Work on distributed, cloud-native systems that process large volumes of data.
Cutting-Edge Technology
- Build with AI, cloud platforms, and scalable architectures shaping legal tech.
Growth and Ownership
- Gain experience owning systems end-to-end across cloud and distributed environments.
Collaborative Culture
- Work in a team focused on knowledge sharing and continuous improvement.
Inclusive Environment
- Diverse perspectives create stronger teams and better outcomes.
Compensation and Benefits
- Competitive salary, benefits, DTO, parental leave, and equity program.
Relativity is committed to competitive, fair, and equitable compensation practices.
This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.
The expected salary range for this role is between following values:
$205,000 and $307,000The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.
Required Skills:
Algorithms, Artificial Intelligence (AI), Big Data, Cloud Computing, Data Science, Deep Learning, Distributed Systems, Natural Language, Python (Programming Language), Software EngineeringCompany
Kcura
Illinois
Company facts come from this company's own listings. We only show what the postings themselves carry.