Software Development Engineer, ML Acceleration, Trainium AI Systems, Annapurna Labs

About the role

Annapurna Labs designs the silicon behind AWS machine learning acceleration. Trainium and Inferentia servers train and serve the largest models our customers run, and before any of that hardware carries customer traffic it has to prove it works. MLA Vetting owns that gate. We build the diagnostic tests that every accelerator server runs before it becomes sellable, and we decide which failures block a host, which route to a technician for repair, and which are noise. That gate only works if we can see through it. We are hiring a Software Development Engineer II to build the data and analytics systems that tell us what our tests are actually doing across the fleet. You will build the pipelines that ingest diagnostic results from tens of thousands of servers, the dashboards that make test behavior legible, and the metrics and alarms that tell us a test has started failing on a hardware generation before it costs us a week of capacity. When the team decides whether a diagnostic has enough evidence to move from observation into blocking production, your data answers that question. This is a software engineering role, not a reporting role. You will write and operate production code, own the alarms and metrics that page us, and build detection for trends nobody is watching yet. You will work directly with hardware, firmware, provisioning, and data center operations teams who act on what you build. You are fluent in Python and SQL and have built production data pipelines at scale. Experience with Amazon Redshift, Apache Spark, workflow orchestration, and dashboarding in Grafana or QuickSight maps directly to what you will work on here. Experience with telemetry or diagnostic data from a large server fleet, or with defining metrics and anomaly detection on operational time-series data, will have you productive faster. Familiarity with server hardware or data center operations is a plus, not a requirement — we will teach you the hardware.

Responsibilities

• Design, build, and operate production data pipelines that ingest diagnostic, telemetry, and repair-ticket data from the Trainium and Inferentia fleet into a warehouse other teams query with confidence.
• Build and own the metrics, alarms, and anomaly detection that surface test regressions, failure-rate shifts, and new failure signatures across hardware generations without a human going looking for them.
• Build dashboards and visualizations that make fleet and test health legible to engineers, hardware partners, and leadership, covering failure rates, failure-signature breakdowns, first pass yield, and repair latency.
• Analyze large-scale fleet data to find root cause behind failure trends, and separate genuine hardware faults from software defects and test noise — a distinction that decides whether a failure reaches a technician or an engineer.
• Define the evidence standard that gates operational decisions, including whether a diagnostic has soaked long enough and cleanly enough in the fleet to move from observation mode into blocking production.
• Improve data quality and pipeline reliability so downstream consumers trust the numbers without re-deriving them.
• Write clear analyses and design documents for technical and non-technical readers, including leadership.

Requirements

• 3+ years of non-internship professional software development experience
• 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
• Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
• Experience programming with at least one software programming language
• Preferred: 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
• Preferred: Bachelor's degree in computer science or equivalent

Skills

• Python
• SQL
• Amazon Redshift
• Apache Spark
• Workflow orchestration
• Dashboarding (Grafana or QuickSight)
• Telemetry/diagnostic data from large server fleets
• Metrics and anomaly detection on operational time‑series data
• Familiarity with server hardware or data center operations (plus)

Benefits

• Health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance, option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• Paid time off
• Parental leave

Pay

Base salary range: $143,700.00 – $194,400.00 USD annually (USA, TX, Austin). Total compensation includes sign‑on payments and restricted stock units (RSUs), with final amount based on experience, qualifications, and location.