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Analog Devices

Senior Engineer, Machine Learning

Analog Devices Wilmington, Massachusetts, United States · $144K–$198K/yr

Semiconductor Manufacturing · 10,001+ employees

Yesterday
machine-learning Senior (5-10 yrs) Full-time United States
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About the role

Develop and maintain distributed training infrastructure and data pipelines for large multimodal datasets. Collaborate with teams to deploy optimized machine learning models to embedded hardware and ensure robust production systems.

What they look for

Python Go Rust C++ Linux Networking Terraform Bicep Kubernetes GPU Azure MLOps CI/CD Quantization ONNX TensorRT

Requirements

Requires 5+ years of production infrastructure experience with strong proficiency in Python and systems languages like Go, Rust, or C++. Candidates must have expertise in Linux, Kubernetes, GPU-enabled runtimes, and cloud-based MLOps workflows.

Benefits

Medical coverage Vision coverage Dental coverage 401k Paid vacation Holidays Sick time Performance-based bonus

Full description

About Analog Devices

Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, AI, and software technologies into solutions that combat climate change, reliably connect humans and the world, and help drive advancements in automation and robotics, mobility, healthcare, energy and data centers. With revenue of more than $11 billion in FY25, ADI ensures today's innovators stay Ahead of What's Possible. Learn more at www.analog.com and on LinkedIn and X.

          

Engineering foundations                                                                                                                                                                  

  - 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                          

                                                                                                                                                                                           

  Collaboration                                                                                                                                                                            

  - Proven record turning research prototypes into supported production systems                                                                                                            

  - Mentoring engineers and driving practice adoption across a team                                                                                                                        

  - Clear design docs and runbooks

For positions requiring access to technical data, Analog Devices, Inc. may have to obtain export  licensing approval from the U.S. Department of Commerce - Bureau of Industry and Security and/or the U.S. Department of State - Directorate of Defense Trade Controls.  As such, applicants for this position – except US Citizens, US Permanent Residents, and protected individuals as defined by 8 U.S.C. 1324b(a)(3) – may have to go through an export licensing review process.

Analog Devices is an equal opportunity employer. We foster a culture where everyone has an opportunity to succeed regardless of their race, color, religion, age, ancestry, national origin, social or ethnic origin, sex, sexual orientation, gender, gender identity, gender expression, marital status, pregnancy, parental status, disability, medical condition, genetic information, military or veteran status, union membership, and political affiliation, or any other legally protected group.

EEO is the Law: Notice of Applicant Rights Under the Law.

Job Req Type: Experienced          

Required Travel: Yes, 10% of the time          

Shift Type: 1st Shift/Days

The expected wage range for a new hire into this position is $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 which is based on personal and company factors.
  • This position includes medical, vision and dental coverage, 401k, paid vacation, holidays, and sick time, and other benefits.

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