Technical AI Engineer (Entry-level, on-site, Oxnard) at SCOSCHE INDUSTRIES INC

Oxnard, California, United States -

Full Time

Start Date

Immediate

Expiry Date

25 May, 26

Salary

50.5

Posted On

24 Feb, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SQL, Data Lakes, Data Pipelines, Machine Learning, Data Engineering, Cloud Platforms, Data Preprocessing, Algorithms, Software Engineering

Industry

Computers and Electronics Manufacturing

Description

Description About Scosche Founded in 1980, Scosche Industries is a family-owned company based in Oxnard, California. We are an award-winning innovator of consumer technology products and car audio accessories, known for quality, value, and exceptional customer service. We offer a collaborative, stable environment where employees can learn, grow, and make a real impact. About the Role: We are seeking a motivated Entry-Level Technical AI Engineer with foundational experience in data lakes and analytics to support the development of AI-driven solutions. This role is ideal for a recent graduate or early-career professional eager to grow skills in machine learning, data engineering, and large-scale data platforms while working alongside experienced engineers and data scientists. The successful candidate will assist in building data pipelines, preparing data for AI models, and supporting the deployment and monitoring of AI solutions. Key Responsibilities: Assist in the design, development, and maintenance of data lake environments to support analytics and AI use cases Support data ingestion, transformation, and validation processes from multiple data sources Help develop, test, and optimize machine learning models under guidance from senior team members Write and maintain clean, well-documented code in Python and SQL Participate in data quality checks, monitoring, and troubleshooting Assist with model evaluation, retraining, and performance tracking Collaborate with cross-functional teams to understand business and technical requirements Document data flows, model logic, and technical processes Stay current with emerging AI, data engineering, and cloud technologies Requirements What We’re Looking For 0–2 years of experience through internships, academic projects, or entry-level roles Foundational knowledge of data lakes or large-scale data storage concepts Proficiency in Python; working knowledge of SQL Basic understanding of machine learning concepts and data preprocessing Familiarity with cloud platforms (AWS, Azure, or GCP) at a beginner level Understanding of data structures, algorithms, and basic software engineering principles Strong curiosity, attention to detail, and willingness to learn Preferred but not required: Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field Hands-on experience with data lake technologies (e.g., AWS S3, Azure Data Lake, Google Cloud Storage) Exposure to big data or analytics tools (e.g., Spark, Databricks) Experience with ML libraries such as Scikit-learn, TensorFlow, or PyTorch Familiarity with ETL/ELT tools or workflow orchestration Coursework or projects involving AI, machine learning, or data engineering Candidates must be legally authorized to work in the United States. The Company does not sponsor employment visas for this position. Compensation & Benefits Hourly pay: $43.25 – $50.00 (non-exempt, overtime eligible) Overtime paid in accordance with California law Medical, dental, and vision benefits 401(k) plan eligibility Paid time off and holidays Why Join Scosche? Hands-on experience with real AI and data projects On-site collaboration with experienced professionals Supportive, team-oriented environment No recruiters please

Responsibilities

The engineer will assist in designing, developing, and maintaining data lake environments to support AI use cases, focusing on data ingestion, transformation, and validation from various sources. They will also help develop, test, and optimize machine learning models under senior guidance and maintain clean, documented code.