Senior ML Software Engineer

Responsibilities

• Feature Development & Delivery: Design, implement, and deploy new features and enhancements to ML products, collaborating with Product and ML Research teams to refine requirements.
• Technical Ownership and Stewardship: Own existing and new production ML products, ensuring alignment of technical investments with business goals and engineering best practices; oversee end‑to‑end reliability and performance.
• Contribute to ML Platform: Help evolve the shared ML platform, driving best practices and shared tooling across all products.
• Operational Excellence: Maintain and improve automated CI/CD pipelines, testing frameworks, and monitoring/logging; conduct comprehensive code reviews to enforce standards and share knowledge.
• Continuous Improvement: Identify and implement opportunities for process, tooling, and system improvements, proactively addressing technical debt and scaling challenges.
• Release Management: Oversee pre‑release testing, coordinate releases, and ensure smooth enablement of new features.
• Leadership: Provide technical guidance and support to other engineers and data scientists, mentor and coach teammates, and foster a culture of collaboration, continuous improvement, and knowledge sharing.
• Act Like an Owner: Proactively identify and resolve blockers, navigate processes, independently seek information, and collaborate with relevant teams to resolve ambiguities.
• Operate with a Strong Sense of Urgency: Consistently prioritize and execute tasks to meet timelines and deliver results.

Qualifications

• 5+ years as an ML‑focused software engineer, MLOps engineer, or similar role with hands‑on production experience.
• Proven expertise with ML model deployment, API design, and integration into production environments.
• Strong Python programming skills and familiarity with ML/data libraries.
• Experience with containerization, orchestration, and AWS cloud services.
• Experience building and operating CI/CD pipelines.
• Monitoring, troubleshooting, and optimizing production ML systems.
• Pre‑release testing and release management experience.
• Demonstrated ability to work independently, navigate ambiguity, and deliver results.
• Excellent communication skills and collaboration across engineering and cross‑functional teams.
• Experience with OpenAPI, FastAPI, or similar frameworks.
• Bonus: Experience with MLFlow, model versioning, and storage.
• Familiarity with Databricks or similar platforms.
• Experience supporting high‑volume, real‑time data products.
• Automated testing and validation frameworks.
• Designing and configuring low‑latency databases for real‑time features (e.g., DynamoDB).
• Experience with Terraform Cloud.
• Experience in large companies, mature engineering teams, or highly regulated industries.
• AI‑assisted coding experience (e.g., GitHub Copilot).

Benefits

• Impact: Own high‑profile ML products, directly influencing reliability, scalability, and evolution of critical production systems.
• Autonomy: End‑to‑end technical ownership, freedom to shape solutions, best practices, and deliver results in a fast‑paced environment.
• Collaboration: Work with a cross‑functional, high‑performing team where expertise is valued and contributions make a real difference.

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