Senior Machine Learning Engineer

Overview

Company Overview

Docusign brings agreements to life. Over 1.5 million customers and more than a billion people in over 180 countries use Docusign solutions to accelerate the process of doing business and simplify people’s lives. With intelligent agreement management, Docusign unleashes business-critical data that is trapped inside of documents. Until now, these were disconnected from business systems of record, costing businesses time, money, and opportunity.

Using Docusign’s Intelligent Agreement Management platform, companies can create, commit, and manage agreements with solutions created by the #1 company in e-signature and contract lifecycle management (CLM).

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.

Responsibility

• 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

Hybrid: Employee divides their time between in-office and remote work. Access to an office location is required. (Frequency: Minimum 2 days per week; may vary by team but will be weekly in-office expectation)

Positions at Docusign are assigned a job designation of either In Office, Hybrid or Remote and are specific to the role/job. Preferred job designations are not guaranteed when changing positions within Docusign. Docusign reserves the right to change a position's job designation depending on business needs and as permitted by local law.

Basic

• 8+ years of professional experience in Machine Learning Engineering or Data Science

• Experience with PyTorch or Tensor Flow, specifically regarding Time Series analysis (forecasting/anomaly detection) and NLP

• Experience building applications using LLMs (RAG pipelines, Lang Chain, 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)

Preferred

• Familiarity with the "three pillars" (Logs, Metrics, Traces) and tools like Prometheus, Grafana, Open Telemetry, or Jaeger

• Experience with frameworks like Auto Gen, CrewAI, or Ray RLlib

• Deep experience with AWS/GCP/Azure and Kubernetes (K8s) orchestration

• A background in control theory or causal inference

Pay for this position is based on a number of factors including geographic location…