Senior Machine Learning Engineer - Responsible AI

Design & Productionize Safety Models: Architect, train, fine-tune, and deploy ML/LLM-based guardrail and moderation models (text, multi-modal, and code) to safeguard user inputs and model outputs across Atlassian products.

Build Robust Evaluation & Benchmarking: Design offline and online evaluation frameworks, adversarial test suites (red-teaming), and LLM-as-a-judge pipelines to measure precision, recall, false-positive rates, and robustness against model drift.

Optimize for Real-Time Serving: Work at the intersection of model accuracy and low latency (P50/P99 targets) to deliver cost-effective, high-throughput safety filtering integrated with our central AI Gateway and inference layers.

Combat Emerging Threats: Research and build defenses against adversarial prompts, jailbreaks, data leakage, hallucinations, and unauthorized tool-calling behaviors in agentic systems.

Drive Global Compliance & Governance: Support technical readiness for enterprise standards and emerging AI regulations (e.g., EU AI Act, ISO 42001 / AIMS, FedRAMP) through verifiable safety controls, audit logging, and watermarking capabilities.

Collaborate & Mentor: Partner with engineering managers, staff engineers, data scientists, and product managers to steer technical strategy; provide code/design reviews and mentor junior engineers across the team.

Your background

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related quantitative field, with 7+ years of industry experience building and deploying machine learning systems in production.

Deep hands-on experience with modern NLP and LLM techniques, transformer architectures, prompt engineering, fine-tuning (e.g., PEFT/LoRA), and model alignment.

Proficiency in Python (and familiarity with Kotlin, Java, or Go) with a track record of writing clean, testable, and performant production-grade code.

Proven experience constructing rigorous evaluation pipelines, benchmarking datasets, and managing precision/recall trade-offs for high-consequence classification or safety systems.

Expertise at telling stories with data and familiarity with at least one visualization tool (e.g. Tableau, R-Shiny, Microstrategy, SAP Business Objects, Looker, etc.)

Experience deploying and scaling ML services in cloud environments (AWS / GCP) using containerized platforms (Kubernetes/Docker) and distributed data processing frameworks (Spark, SQL).

Strong analytical problem-solving skills, ability to navigate ambiguity, and clear communication skills to articulate ML trade-offs to technical and non-technical stakeholders.

An agile development mindset focused on constant iteration, solving problems from first principles, and navigating ambiguity.

Experience specifically in Trust & Safety, Content Moderation, or AI Red-Teaming / Adversarial Robustness.

Hands-on experience optimizing inference engines (e.g., Triton Inference Server, vLLM, TensorRT-LLM) or model quantization techniques for low-latency serving.

Familiarity with AI governance frameworks, content provenance/watermarking, or regulatory compliance standards (e.g., EU AI Act, NIST AI RMF, ISO 42001).

Experience with large-scale streaming pipelines (e.g., Kafka, Kinesis) or graph-based entity systems.

Benefits & Perks

Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits.

At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together.

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