Machine Learning Operations Engineer

Who is Recruiting from Scratch :

Recruiting from Scratch is a specialized talent firm dedicated to helping companies build exceptional teams. We partner closely with our clients to deeply understand their needs, then connect them with top‑tier candidates who are not only highly skilled but also the right fit for the company’s culture and vision. Our mission is simple : place the best people in the right roles to drive long‑term success for both clients and candidates.

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Title of Role

Machine Learning Operations Engineer

Location

Phoenix, AZ (On‑site)

Company Stage of Funding

Early‑Stage, Venture‑Backed

Office Type

On‑site, Full‑Time

Salary

Competitive + Equity

Company Description

We’re representing a

defense technology company

building next‑generation

autonomous swarm systems

for unmanned ground vehicles (UGVs). The company is applying cutting‑edge

machine learning and edge AI

to deliver low‑cost coordinated robotic fleets capable of executing complex missions across multiple domains. The leadership team brings decades of experience in self‑driving vehicles, aerospace, and defense, and the company is rapidly scaling its engineering team in Phoenix, AZ to meet growing demand.

What You Will Do

As a

Machine Learning Operations Engineer , you’ll design, build, and maintain the ML infrastructure that powers perception and autonomy across vehicle swarms. You will :

Design and implement

end-to-end ML pipelines

for training, validation, and deployment of perception models.

Build robust

data management systems

for large‑scale sensor data (cameras, LiDAR, IMU) from field operations.

Implement

model monitoring, A / B testing, and performance tracking

systems for deployed models.

Develop CI / CD pipelines for

model versioning, testing, and deployment

to fleets of autonomous UGVs.

Create distributed computing solutions for

large‑scale data processing and model training .

Build internal tools for

data annotation, evaluation, and performance visualization .

Collaborate with perception engineers, robotics teams, and field ops to ensure seamless deployment.

Ideal Background

2+ years of industry experience in

MLOps, DevOps, or ML infrastructure .

Bachelor’s degree in computer science, engineering, or related field.

Strong experience with ML pipeline orchestration tools (e.g.,

Kubeflow, MLflow ).

Proficiency with

Docker, Kubernetes , and cloud platforms (AWS, GCP, or Azure).

Strong

Python programming

and

Linux system administration

skills.

Experience with model serving frameworks ( TensorRT, ONNX Runtime, TorchServe ).

Familiarity with data versioning and experiment tracking tools (e.g.,

Weights & Biases, Neptune ).

Experience with monitoring and logging systems ( Prometheus, Grafana, ELK stack ).

Strong organizational and communication skills; thrives in a

fast‑paced startup environment .

Eligible to work on

export‑controlled projects

and willing to

relocate to Phoenix, AZ .

Compensation and Benefits

Salary : Competitive (commensurate with experience)

Equity : Meaningful early‑stage ownership stake

Work Setup : On‑site in Phoenix, AZ (relocation assistance available)

Other Benefits :

Direct ownership of core ML infrastructure powering real‑world autonomy

Opportunity to work across defense, robotics, and swarm AI systems

Mission‑driven, collaborative environment with leadership experienced in frontier robotics

This role is ideal for engineers passionate about scaling ML infrastructure, deploying cutting‑edge models in the field, and building the backbone for autonomous swarm robotics in a fast‑moving defense technology company.

Salary Range

$160,000-$200,000 base.

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