Staff Machine Learning Engineer

About the job

Responsibilities

• Lead end-to-end ML system lifecycle from requirements to release for complex, cross-functional initiatives.
• Architect and build the AI/ML layer for the DFM AI + IQE integration with Teamcenter and Designcenter, including real-time ML serving and low-latency signal paths.
• Define data contracts for model inputs/outputs and implement MLOps, governance, and observability for a mission-critical integration.
• Develop cloud-based production systems for real-time endpoints and MLOps, integrated with broader systems.
• Navigate complex technical challenges and deliver solutions meeting business and technical objectives.
• Proactively identify opportunities, own new processes, and develop multi-quarter roadmaps.
• Apply best practices in automated testing, parallel/distributed computing, and secure software development.
• Collaborate with cross-functional teams to translate requirements into technical solutions.
• Mentor other engineers through design and code reviews.
• Stay current with ML/AI advancements and integrate relevant new approaches, tools, and frameworks.

Requirements

• Bachelor's degree in a STEM field or equivalent experience.
• 6-8 years of experience in machine learning engineering with a track record of delivering complex ML systems in production.
• Deep expertise in ML/AI technologies including Gradient Boosting, Deep Learning, and/or Generative AI frameworks.
• Hands-on experience deploying real-time ML products at scale in cloud environments (AWS preferred), including auto-scaling, monitoring, and alerting.
• Strong proficiency in Python and advanced ML/AI frameworks (TensorFlow, PyTorch, or similar).
• Solid grounding in software engineering fundamentals, data structures, and algorithms.
• Demonstrated experience with MLOps practices (model monitoring, drift detection, automated retraining/redeployment).
• Proficiency with CI/CD pipelines (e.g., Github actions), test-driven development, and infrastructure as code (e.g., Terraform).
• Experience profiling and optimizing ML model deployments for latency and throughput.
• Ability to operate independently on ambiguous assignments and communicate effectively across audiences.
• Experience with state-of-the-art modeling techniques including transformers, self-supervised pre-training, LLMs, or generative AI.
• Knowledge of containers, container orchestration (Kubernetes), and cloud-native distributed systems.

Benefits

• Bonus potential
• 401(k) match
• Medical insurance
• Dental insurance
• Vision insurance
• Life insurance
• Disability insurance
• Generous paid time off (vacation, sick leave, floating and fixed holidays)
• Maternity and bonding leave
• Employee Assistance Program (EAP)
• Other wellbeing resources