Senior Machine Learning Engineer

Disclaimer: Due to the sensitive nature of our engineering work, Anno.ai enforces strict digital footprint and identity verification. We actively monitor for synthetic profiles, proxy networks, and AI interview assistants; any fraudulent activity will result in immediate disqualification.

Position Overview

As a Senior Machine Learning Engineer at Anno.ai, you will design, develop, test, document, deploy, and maintain production machine learning and statistical modeled software to automate processes and streamline our customer’s mission operations. MLEs work directly with product, user-facing, hardware, and platform teams to deliver the highest quality products. You will join a team of beasts known as “Annomals” are notable for their practical, mission-driven, and fun demeanor. MLEs work directly with product, user-facing, hardware, and platform teams to deliver the highest quality products, and because of these diverse interfaces, we value good, seasoned judgment in your approach to management, your career growth, and maintaining ethical and responsible practices.

For this opportunity we are looking for MLEs who have a fairly uniform distribution of talent across a breadth the range of machine learning tasks and skills. You are an experienced MLE, part solid software engineer, and part modeling expert. You have been through the trenches and bring key knowledge and intuition through your combination of training and experience.

Candidates need to be able to obtain and maintain U.S. Government security clearance (U.S. citizenship required).  Candidates must be able to travel up to 20% of the time.

What You Will Do

Operationalize machine learning models by building and maintaining robust, scalable pipelines for training, evaluation, deployment, and lifecycle management across cloud, on-prem, and edge compute environments

Work closely with autonomy researchers, software engineers, systems teams, and field operators to translate mission requirements into deployable ML capabilities

Implement automated CI/CD workflows tailored to ML systems, ensuring repeatable experiments, reliable packaging, and continuous delivery of both up to date models and associated data pipelines

Manage ML runtime infrastructure using containerization and orchestration frameworks (e.g., Docker, Kubernetes) and incorporating model serving platforms (e.g., Seldon, KServe, BentoML)

Develop monitoring systems to track model health, performance, data drift, system utilization, and mission relevance using tools such as Prometheus, Grafana, and ELK/EFK stacks

Ensure ML deployments meet defense, customer, and platform security requirements, with emphasis on data integrity, traceability, and operational reliability

Evaluate and integrate emerging MLOps, distributed training, and edge inference technologies to enhance reproducibility, extensibility, scalability, and deployment speed of ML systems

Required Qualifications

Bachelor’s degree in Computer Science, Electrical Engineering, Data Science, or a related technical field (Master’s preferred)

5+ years of professional experience in software engineering, machine learning engineering, MLOps, or related roles

Experience operationalizing ML systems at production scale, including model training, versioning, packaging, deployment, and monitoring

Strong proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow)

Hands-on experience with MLOps frameworks and workflow tooling (e.g., MLflow, Kubeflow, Airflow, DVC, BentoML)

Experience deploying containerized ML services using Docker and orchestrating workloads using Kubernetes (including air-gapped or constrained deployments)

Understanding of CI/CD workflows and DevOps practices applied to ML systems (e.g., Git, Code Review, Metrics Evaluation)

Familiarity with monitoring, observability, and logging platforms (e.g., Prometheus, Grafana, ELK/EFK)

Ability to obtain and maintain U.S. Government security clearance (U.S. Citizenship required)

Ability to travel up to 20%

Preferred Qualifications

Experience with deploying models and associated runtimes to Edged Devices

Experience optimizing models for memory and CPU constrained systems (e.g., embedded systems, microcontrollers)

Prior experience supporting U.S. Department of War programs, cUAS systems, or mission-critical autonomous platforms

Experience working with diverse or atypical data sources (e.g., Audio/Acoustics, RF signals, EO/IR imagery)

Experience deploying and optimizing ML inference on edge or resource-limited compute systems

Experience with Explainable/Auditable AI/ML tools and interpretable model design

Experience with AI Software Development Tools (e.g., GitHub CoPilot, Claude)