Senior Machine Learning Engineer

Docusign • San Francisco, CA

9d • Hybrid

About The Position

We are looking for a Senior Machine Learning Engineer to redefine how we operate our global services. You won't just be building dashboards; you will be building the "brain" of our infrastructure.
We are moving beyond simple anomaly detection. We are building a self-healing ecosystem where Multi-Agent Systems and Reinforcement Learning (RL) loops work in tandem with Large Language Models (LLMs) to not only detect incidents in real-time but to troubleshoot and resolve them autonomously.
If you are passionate about applying complex AI architectures to massive datasets (billions of telemetry points) to solve real-world reliability challenges, this is the role for you.
This position is an individual contributor role reporting to the Sr. Director, Software Engineering.

Requirements

• 8+ years of professional experience in Machine Learning Engineering or Data Science
• Experience with PyTorch or TensorFlow, specifically regarding Time Series analysis (forecasting/anomaly detection) and NLP
• Experience building applications using LLMs (RAG pipelines, LangChain, vector databases) specifically for technical domains (code analysis, log parsing)
• Experience with RL concepts (policies, rewards, agents) and experience applying them to optimization or control problems
• Experience with distributed data processing and streaming technologies (Apache Spark, Kafka, Flink)
• Expereience with software engineering fundamentals (Python, C++, or Go), CI/CD for ML, and experience deploying models via APIs (FastAPI, Triton Inference Server)

Nice To Haves

• Familiarity with the "three pillars" (Logs, Metrics, Traces) and tools like Prometheus, Grafana, OpenTelemetry, or Jaeger
• Experience with frameworks like AutoGen, CrewAI, or Ray RLlib
• Deep experience with AWS/GCP/Azure and Kubernetes (K8s) orchestration
• A background in control theory or causal inference

Responsibilities

• Design and implement autonomous multi-agent systems using Reinforcement Learning (RL) loops that can interact with our infrastructure to perform safe, automated remediation actions
• Build GenAI agents capable of digesting logs, traces, and metrics to provide "Human-in-the-loop" root cause analysis and conversational debugging for our SREs
• Develop and deploy deep learning models (Transformers, LSTMs, etc.) for forecasting and anomaly detection on high-cardinality, high-volume time series data
• Optimize inference pipelines to run with low latency on streaming telemetry data (Kafka/Flink), ensuring we catch issues the moment they happen
• Own the lifecycle of your models—from feature engineering on petabyte-scale datasets to training, deployment, and monitoring in production Kubernetes environments
• Collaborate with Applied Scientists to translate bleeding-edge research (e.g., causal inference, decision transformers) into production-hardened AIOps tools

Benefits

• Bonus: Sales personnel are eligible for variable incentive pay dependent on their achievement of pre-established sales goals. Non-Sales roles are eligible for a company bonus plan, which is calculated as a percentage of eligible wages and dependent on company performance.
• Stock: This role is eligible to receive Restricted Stock Units (RSUs).
• Paid Time Off: earned time off, as well as paid company holidays based on region
• Paid Parental Leave: take up to six months off with your child after birth, adoption or foster care placement
• Full Health Benefits Plans: options for 100% employer paid and minimum employee contribution health plans from day one of employment
• Retirement Plans: select retirement and pension programs with potential for employer contributions
• Learning and Development: options for coaching, online courses and education reimbursements
• Compassionate Care Leave: paid time off following the loss of a loved one and other life-changing events

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed