Sr. ML Engineer (MLOps)

Accountabilities

• Build and maintain the tooling, platforms, and backend services that power machine learning and generative AI solutions in production.
• Develop services supporting model training, inference, deployment, monitoring, and other stages of the ML/AI lifecycle.
• Build and maintain model evaluation metrics, testing frameworks, and supporting infrastructure to assess the quality and reliability of ML and generative AI systems.
• Develop APIs and production services that expose machine learning and AI capabilities to applications and users.
• Deploy and manage applications supporting different components of the AI/ML software development lifecycle.
• Design scalable, maintainable systems using modern software engineering and MLOps practices.
• Develop high-quality production code, primarily in Python, while contributing to architecture, implementation, testing, and operational excellence.
• Collaborate with data scientists, data analysts, product managers, and other technical and domain specialists to translate complex requirements into effective technical solutions.
• Take ownership of cross-functional initiatives from initial design through deployment and ongoing improvement.
• Provide technical guidance, mentorship, and engineering best practices to other team members.
• Help evaluate and introduce new technologies that can improve ML development, deployment, testing, and operational workflows.
• Contribute to the development of retrieval-augmented generation solutions and vector-based AI infrastructure where appropriate.
• Work with sensitive and business-critical data while maintaining strong standards for security, reliability, privacy, and system quality.
• Balance engineering quality, scalability, and maintainability with business priorities and delivery timelines.

Requirements

• 6+ years of experience deploying machine learning or AI solutions in production environments.
• Strong Python programming skills and a demonstrated ability to write clean, maintainable, production-quality code.
• Experience designing and developing RESTful APIs and production backend services.
• Experience defining and working with Protobuf messages and service interfaces.
• Hands-on experience with Docker and deploying applications to Kubernetes.
• Experience working with relational databases and low-latency data stores.
• Experience using Celery or comparable technologies for distributed or asynchronous task processing.
• Strong understanding of machine learning and AI production workflows, including deployment, inference, evaluation, testing, and operational support.
• Demonstrated ability to learn new technologies quickly and apply them effectively to complex engineering challenges.
• Strong interest in building ML- and AI-powered products and production systems from the ground up.
• Excellent communication skills and the ability to build consensus across technical and non-technical stakeholders.
• Strong organizational and prioritization skills, with the ability to turn complex problems into clear, actionable priorities.
• Ability to take ownership, work independently, and drive cross-functional initiatives to completion.
• Thoughtful approach to balancing engineering quality, technical debt, business priorities, and deadlines in a fast-paced environment.
• Experience building retrieval-augmented generation (RAG) solutions is preferred.
• Experience setting up and maintaining vector databases is preferred.
• Production software development experience with Java or Kotlin is a plus.
• Experience building and operating solutions on AWS or other cloud infrastructure is preferred.
• Experience working with highly sensitive data, particularly within healthcare or similarly regulated environments, is beneficial.

Benefits

• Salary: Annual base salary range of $143,000–$197,000, with placement based on skills, qualifications, experience, location, and other job-related factors.
• Additional compensation: Eligibility for discretionary bonuses and equity through restricted stock units.
• Healthcare: Comprehensive medical, dental, and vision coverage, along with FSA/HSA options.
• Insurance: Life and disability insurance coverage.
• Mental health support: Access to coaching and therapy services as part of the employee wellness offering.
• Retirement: 401(k) plan with up to a 3% employer match.
• Paid time off: Competitive vacation, sick leave, and company holiday policies.
• Parental support: Paid parental leave.
• Technology support: Monthly technology allowance.
• Well-being: Wellness perks, community activities, employee celebrations, and additional engagement programs.
• Work environment: Fully remote position with opportunities to collaborate across multidisciplinary teams.
• Professional growth: Hands-on exposure to advanced ML, generative AI, MLOps, cloud infrastructure, and production-scale engineering challenges.