Drata is building the trust layer between great companies - automating compliance, managing risk, and helping organizations prove trust continuously as they scale. We're Dratanauts: a global crew of 600+ professionals united by a culture that rewards integrity, ownership, and raising the bar, no matter where in the world we're working from. Why Join the Drata Team? At Drata, you're not maintaining legacy compliance software - you're building the agentic AI platform defining what trust looks like for the next generation of companies. Here's what makes the work itself worth showing up for: Problems without a playbook: You'll work at the edge of AI and security, building agentic governance, continuous compliance, and real-time trust verification to solve problems that don't have an established answer yet. You're writing it as you go. Real ownership, not just process: Our values center on owning outcomes and raising the bar, not checking boxes. You're expected to have opinions and back them. A seat at the table: Your perspective is unique and valued. Open debate and diverse viewpoints are built into how decisions actually get made here, at every level. Growth at rocketship speed: Drata is scaling fast, which means scope grows fast too. High performers get more ownership, visibility, and experience. A crew, not just coworkers: Dratanauts consistently describe a "come as you are" culture with sharp, curious people—the kind of team that makes hard problems genuinely fun to solve. See what they say here and follow us on LinkedIn for company news, employee stories, and career updates. Job Summary: The Senior Platform Engineer II, AI Tooling on the Developer Experience Team (part of Foundation Engineering group) will lead the design and delivery of the internal AI platform that makes Drata's engineers more efficient - the tools, agents, and integrations that turn AI coding agents (and the rest of the AI dev stack) into a paved road for everyday engineering work. This is not a
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Role overview
Job description
JOB DESCRIPTION:
Position Overview
The AI Platform Engineer builds and operates the machine learning and generative AI platform used by teams across Abbott Cancer Diagnostics. You'll own the full model lifecycle in production — data and feature pipelines, training and experimentation, evaluation and promotion, serving, and monitoring — along with the platform services, compute and tooling underneath it. This is hands-on infrastructure work backed by solid platform engineering practice: making inference fast and cheap, making the path from experiment to production repeatable and auditable, and shipping interfaces other engineers can build on — in support of software that ultimately reaches patients.
Essential Duties
Include, but are not limited to, the following:
Build and maintain data, feature, and training pipelines for ML and LLM workloads — ingestion, transformation, fine-tuning, distributed training, and reproducible experiment execution with lineage tracked from dataset and code to resulting model.
Implement automated evaluation and promotion gates — performance benchmarks, regression checks, and validation criteria that determine whether a model advances toward production.
Automate the model lifecycle end to end through CI/CD and GitOps: packaging, promotion across environments, progressive rollout, and rollback.
Build and operate production model-serving infrastructure for LLMs and predictive models, including inference optimization, autoscaling, and low-latency serving across multiple model formats and runtimes.
Architect and manage GPU infrastructure — scheduling, autoscaling, resource isolation, and utilization efficiency for training and inference workloads.
Instrument the platform and the models on it — structured logging, telemetry, drift detection, and cost tracking — and build the triggers and pipelines that close the loop into retraining and revalidation.
Extend model, dataset, and artifact registries, metadata systems, and versioning so every deployed model has a traceable, auditable history.
Build the developer-facing surface of the platform: APIs, SDK components, templates, documentation, and runbooks that make it self-service, backed by well-tested code and active participation in design and code review.
Uphold company mission and values through accountability, innovation, integrity, quality, and teamwork.
Maintain regular and reliable attendance.
Act with an inclusion mindset and model these behaviors for the organization.
Minimum Qualifications
Bachelor's degree in Computer Science, Engineering, AI/ML, or a related field; or equivalent practical experience.
3+ years building and operating production software, including significant work on ML or AI infrastructure.
Strong Python, including software engineering fundamentals — automated testing, code review, and designing code others will read and extend.
Experience with the ML model lifecycle: pipelines that carry a model from training through evaluation, deployment, monitoring, and retraining.
Kubernetes experience — deploying, scaling, and debugging containerized workloads on a major cloud provider (AWS preferred).
Experience with CI/CD, GitOps-based delivery, and infrastructure automation.
Preferred Qualifications
ML workflow orchestration and experiment tracking (for example Kubeflow, Argo Workflows, Airflow, MLflow, or Weights & Biases).
GPU infrastructure at scale: scheduling, multi-tenancy and resource isolation, autoscaling, and inference optimization such as quantization, batching, or graph compilation (for example ONNX Runtime or TensorRT).
Feature stores, data versioning, or dataset lineage tooling.
Progressive delivery for models — canary, shadow, or A/B deployment and automated rollback.
Model monitoring and drift detection, including automated retraining triggers.
Model governance and reproducibility practices: lineage tracking, audit trails, and approval workflows.
Kubernetes-native serverless and event-driven autoscaling (for example Knative or KEDA).
Experience building APIs or shared libraries consumed by other engineering teams.
Production experience in an additional systems language such as Go or Java.
Fine-tuning foundation models, or building distributed training pipelines.
Experience delivering software in a regulated environment (HIPAA, CLIA, GxP, SOC 2).
We are an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard to age, color, creed, disability, gender identity, national origin, protected veteran status, race, religion, sex, sexual orientation, and any other status protected by applicable local, state, or federal law. Applicable portions of the Company’s affirmative action program are available to any applicant or employee for inspection upon request.
The base pay for this position is
$61,300.00 – $122,700.00In specific locations, the pay range may vary from the range posted.
JOB FAMILY:
Product Development
DIVISION:
ONCO Cancer Diagnostics
LOCATION:
United States > Madison : 5505 Endeavor Ln
ADDITIONAL LOCATIONS:
WORK SHIFT:
Standard
TRAVEL:
Yes, 10 % of the Time
MEDICAL SURVEILLANCE:
No
SIGNIFICANT WORK ACTIVITIES:
Continuous sitting for prolonged periods (more than 2 consecutive hours in an 8 hour day), Keyboard use (greater or equal to 50% of the workday)Abbott is an Equal Opportunity Employer of Minorities/Women/Individuals with Disabilities/Protected Veterans.
EEO is the Law link - English: http://webstorage.abbott.com/common/External/EEO_English.pdf
EEO is the Law link - Espanol: http://webstorage.abbott.com/common/External/EEO_Spanish.pdf
What they are looking for
Skills & requirements
Qualification
Essential Duties Include, but are not limited to, the following: Build and maintain data, feature, and training pipelines for ML and LLM workloads — ingestion, transformation, fine-tuning, distributed training, and reproducible experiment execution with lineage tracked from dataset and code to resulting model; Minimum Qualifications Bachelor's degree in Computer Science, Engineering, AI/ML, or a related field; or equivalent practical experience; All qualified applicants will receive consideration for employment without regard to age, color, creed, disability, gender identity, national origin, protected veteran status, race, religion, sex, sexual orientation, and any other status protected by applicable local, state, or federal law
Hiring company
Abbott
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