About the role

What will you do at Apkudo?

Machine Learning Engineer, Device AI

The Device AI team builds the computer vision and machine learning systems that

power automated cosmetic inspection, grading, and quality assessment for devices

at scale. As a Machine Learning Engineer, you'll help build and maintain the

computer vision and ML pipelines that power our automated inspection systems,

working under the guidance of senior engineers on the team. You'll get hands-on

experience across the full ML lifecycle — from data exploration and model

training through evaluation and production deployment — while growing your

skills in a collaborative, production-focused environment.

What You'll Do

* Built and maintained components of computer vision and machine learning
pipelines for automated visual inspection and quality grading, with guidance
from senior team members.
* Support model development activities: data exploration and cleaning, feature
engineering, model training, and evaluation against defined metrics.
* Write clean, well-tested Python code for production systems, following team
standards and best practices.
* Participate in code review as both a reviewer and reviewee, learning from
feedback and building good engineering habits.
* Help build and maintain model evaluation and monitoring tooling, and flag
performance or data quality issues as they arise.
* Work with data labeling and annotation pipelines, helping to improve training
data quality under senior guidance.
* Collaborate with product and operation partners to understand requirements
and translate them into technical tasks.
* Document your work clearly, including model experiments, pipeline changes,
and design decisions.
* Participate in sprint planning, stand-ups, and retrospective, and contribute
to estimation for your own work,
* Take on increasing ownership over time as you build context on the team’s
system and domain.
What You Bring
* BS in Computer Science, Data Science, or a related field (equivalent hands-on
experience can substitute).
* 3-5 years of professional experience as a Machine Learning Engineer, Data
Scientist, or in a closely related applied ML role.
* Solid Python proficiency, with experience writing production or
near-production code (not just research notebooks).
* Some hands-on exposure to computer vision, image processing, or applied ML
models, including work that reached a production or near-production
environment.
* Experience with Python image processing libraries, such as OpenCV or PIL.
Familiarity with lifecycle concepts and tools in the AWS environment.
* Working knowledge of SQL and relational databases (PostGreSQL or equivalent),
basic familiarity with cloud infrastructure (AWS or equivalent).
* Exposure to ML engineering functions: model evaluation, version control for
models/data, and the experimentation-to-production workflow.
* Experience with Python image processing libraries, such as OpenCV or PIL.
* Working knowledge of SQL and relational databases (PostgreSQL or equivalent);
basic familiarity with cloud infrastructure (AWS or equivalent).
* Exposure to ML engineering fundamentals: model evaluation, version control
for models/data, and the general experimentation-to-production workflow.
* Familiarity with Generative AI solutions, agentic systems, and the use of AI
in development workflows such as Spec-Driven Development or Intent-Driven
Development. Experience with any AI coding tools is a plus.
* Some experience working within agile teams (sprint planning, stand-ups,
retrospectives).
* Good written and verbal communication skills; able to explain your work
clearly to teammates.
* A collaborative mindset and eagerness to learn from more senior engineers on
the team.
* Exposure to edge deployment, on-device inference, or hardware-in-the-loop AI
systems is a plus, but not required.
.

Which skills does this role require?

PythonComputer VisionMachine LearningOpenCVPILSQLPostgreSQLAWSModel EvaluationData ExplorationFeature EngineeringModel TrainingVersion ControlGenerative AIData ScienceSpec-Driven DevelopmentIntent-Driven DevelopmentQuality AssessmentAutomated InspectionData LabelingAnnotation PipelinesProduction DeploymentCloud InfrastructureEngineeringDevice AILLMsA/B Testing

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