Senior Engineer, AI/ML Software

Responsibilities

• Design, develop and maintain scalable and efficient AI/ML systems that solve complex problems.
• Collaborate with cross-functional teams, including product managers, UX designers, and software engineers to gather requirements and design appropriate solutions.
• Lead the development of end-to-end AI/ML pipelines, including data preprocessing, model training, prompt engineering, and performance evaluation.
• Participate in code reviews, architecture discussions, and technical planning.
• Optimize AI/ML model performance and ensure robustness, fairness, and explainability.
• Monitor AI/ML system performance, troubleshoot issues, and provide timely resolutions.
• Implement data security and privacy measures to protect sensitive information.
• Continuously evaluate and recommend improvements to our data infrastructure, tools, and processes.
• Stay up to date with emerging trends and technologies in the fields of AI and ML.
• Mentor junior engineers and contribute to best practices in AI/ML development.
• In this role, these duties are performed in support of the team's AI/ML systems for tactile sensing and robot learning: developing end-to-end AI/ML pipelines over large multimodal datasets, including video, tactile, and time-series sensor data; training, evaluating, and optimizing models, including distributed multi-node training with checkpointing and fault recovery; optimizing and deploying trained models to their target compute environments, including GPU-accelerated and embedded runtimes; monitoring deployed model and system performance and resolving issues; and mentoring engineers on AI/ML development practices.

Requirements

• 5+ years production infrastructure
• Strong Python plus one systems language (Go/Rust/C++)
• Expert Linux, networking, and debugging across the full stack
• Infrastructure-as-code (Terraform/Bicep) and containers, including GPU-enabled runtimes
• Cloud & orchestration: Production cloud experience (Azure preferred): IAM, storage, managed compute, cost control
• Kubernetes with GPU workloads: scheduling, autoscaling, quotas
• MLOps: Distributed training infrastructure: multi-node GPU, checkpointing, fault recovery
• Experiment tracking, model registry, dataset/artifact versioning
• ML CI/CD with automated evaluation gates and rollback
• Data pipelines for large multimodal datasets (video, tactile, time series)
• Observability: GPU utilization, drift, data quality
• Edge deployment: Model optimization for constrained targets: quantization, ONNX, TensorRT
• Deployment to embedded hardware (Jetson or similar) with OTA updates and rollback
• Awareness of latency constraints in closed-loop control
• Proven record turning research prototypes into supported production systems
• Mentoring engineers and driving practice adoption across a team
• Clear design docs and runbooks

Benefits

• Medical, vision and dental coverage
• 401k
• Paid vacation, holidays, and sick time
• Other benefits

Pay

Expected wage range for a new hire: $144,000 to $198,000. Actual wage offered may vary depending on work location, experience, education, training, external market data, internal pay equity, or other bona fide factors. This position qualifies for a discretionary performance-based bonus based on personal and company factors.

Schedule

Shift type: 1st Shift/Days. Required travel: Yes, approximately 10% of the time.