Machine Learning Engineer - Early Career
Jobright.ai · United States
- Hybrid
- Full-time
- $90,000 / year
- United States
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Job highlights
- Build and scale AI agents from prototype to production.
- Develop scalable infrastructure for AI agent deployment.
- Optimize LLM pipelines for performance and efficiency.
- Implement automated systems for model reliability.
- Architect APIs connecting AI models to user products.
About the role
About Jobright.ai
Jobright is your personal AI job search agent that transforms the job search process into a fast, expert-guided journey. We are seeking an AI Engineer to build and scale business-facing AI agents, managing the entire lifecycle from prototype to production.Why Join Us
- Build real, production AI agents used by real users
- High ownership and impact
- Work at the intersection of AI, agents, and product
- Shape how people experience AI-driven job search
Responsibilities
- Design, build, and maintain the scalable infrastructure required to deploy and serve production-grade AI agents.
- Implement and optimize Large Language Model (LLM) pipelines, focusing on latency reduction, throughput, and efficient resource utilization.
- Develop automated systems for model monitoring, testing, and continuous integration to ensure the reliability of our AI agents.
- Optimize data ingestion and processing layers to support real-time agent responsiveness and complex RAG (Retrieval-Augmented Generation) architectures.
- Architect and refine APIs and backend services that bridge the gap between AI models and the user-facing product.
Qualifications
Required
- Recent graduate or early-career professional (0–2 years of experience) with a degree in Computer Science, Software Engineering, or a related technical field.
- Strong proficiency in Python and experience with backend frameworks (such as FastAPI, Flask, or Django).
- Practical experience with machine learning frameworks (PyTorch or TensorFlow) and a solid understanding of software engineering best practices (version control, CI/CD, unit testing).
- Familiarity with the deployment of LLMs and an understanding of the infrastructure required to support autonomous agents.
- Must live in and be authorized to work in the United States.
Preferred
- Previous internship or project experience in ML Ops, backend engineering, or distributed systems within an AI-focused company.
- Hands-on experience with containerization (Docker, Kubernetes) and cloud infrastructure (AWS, GCP, or Azure).
- Knowledge of vector databases (such as Pinecone, Milvus, or Weaviate) and their role in production AI systems.
- Strong foundation in SQL and NoSQL database management for high-scale data handling.
Key skills/competency
- Machine Learning Engineering
- AI Agents
- LLM Pipelines
- Python
- Backend Frameworks
- MLOps
- Cloud Infrastructure
- Containerization
- Data Ingestion
- API Development
Skills & topics
- Machine Learning Engineer
- AI
- LLM
- Python
- Backend Development
- MLOps
- Cloud Computing
- Software Engineering
- Data Engineering
- API Development
How to get hired
- Tailor your resume: Highlight Python, ML frameworks, and backend experience.
- Showcase projects: Emphasize internships or personal projects in ML Ops or AI.
- Prepare for technical interviews: Brush up on Python, LLMs, and system design.
- Understand the role: Research Jobright.ai's AI agents and their impact.
Technical preparation
Master Python and backend frameworks.,Practice with PyTorch or TensorFlow.,Understand LLM deployment and infrastructure.,Familiarize with Docker and cloud platforms.
Behavioral questions
Describe a challenging project you completed.,How do you handle tight deadlines?,How do you collaborate with a team?,Tell me about a time you learned a new technology.
Frequently asked questions
- What are the key responsibilities for an early-career Machine Learning Engineer at Jobright.ai?
- As an early-career Machine Learning Engineer at Jobright.ai, you'll focus on designing, building, and maintaining scalable infrastructure for AI agents. This includes optimizing LLM pipelines, developing automated monitoring systems, processing data for real-time responsiveness, and architecting APIs that connect AI models to the user-facing product.
- What technical skills are most important for this Machine Learning Engineer role at Jobright.ai?
- Strong proficiency in Python and experience with backend frameworks like FastAPI, Flask, or Django are essential. You'll also need practical experience with ML frameworks such as PyTorch or TensorFlow, and a good understanding of software engineering best practices, LLM deployment, and supporting infrastructure for autonomous agents.
- Does Jobright.ai offer opportunities for growth for early-career Machine Learning Engineers?
- Jobright.ai emphasizes high ownership and impact for all team members. Working at the intersection of AI, agents, and product, you'll have the opportunity to shape user experiences and contribute directly to production AI agents, offering significant growth potential for early-career professionals.
- What is the company culture like at Jobright.ai for a Machine Learning Engineer?
- Jobright.ai fosters a culture where you build real AI agents used by real users. The environment offers high ownership and impact, allowing you to work at the cutting edge of AI and shape the future of AI-driven job search.
- What are the preferred qualifications for the Machine Learning Engineer position at Jobright.ai?
- Preferred qualifications include previous internship or project experience in ML Ops, backend engineering, or distributed systems within an AI-focused company. Hands-on experience with containerization (Docker, Kubernetes), cloud infrastructure (AWS, GCP, Azure), vector databases, and SQL/NoSQL database management are also highly valued.
- Is this Machine Learning Engineer role at Jobright.ai remote or on-site?
- The job description states that candidates must live in and be authorized to work in the United States, but does not explicitly mention remote or on-site work. It is likely a hybrid or on-site role, but clarification may be needed.