Machine Learning Engineer

Job Description

411_3485056

What You’ll Build

In this role, you’ll build and operationalize machine learning and AI capabilities from idea to production and deliver measurable product value. You’ll develop data move pipelines, training and inference workflows, model‑serving services, APIs, and evaluation frameworks for use cases such as recommendation, forecasting, anomaly detection, classification, NLP, semantic search, and generative AI.

Your work will include feature engineering, experiment design, model tuning, offline and online validation, and integration of models into scalable product architectures. You’ll help improve LLM‑based workflows, including prompt design, retrieval‑augmented generation, vector search, guardrails, and response quality evaluation. You’ll also strengthen the engineering backbone around AI through CI/CD, monitoring, observability, testing, and model lifecycle automation so solutions are reliable, cost‑aware, secure, and ready for enterprise scale.

What You Bring

• Strong programming skills in Python, along with working knowledge of Java or Go, for building production‑grade services and APIs
• Solid understanding of machine learning fundamentals, including supervised and unsupervised methods such as classification, regression, clustering, ranking, and recommendation systems
• Hands‑on experience with deep learning frameworks (e.g., PyTorch or TensorFlow) for model training, fine‑tuning, and inference
• Strong capabilities in data preparation, feature engineering, data validation, and model evaluation using appropriate offline and online metrics
• Experience building, deploying, and integrating ML models into production systems through batch, real‑time, or streaming pipelines
• Familiarity with generative AI concepts, including LLMs, embeddings, vector databases, prompt engineering, and retrieval‑augmented generation, and the ability to apply them in practical use cases
• Working knowledge of MLOps and modern data infrastructure, including experiment tracking, model versioning, CI/CD, and tools such as Spark, Kafka, Airflow, and feature stores
• Experience operating ML systems in production, including monitoring for drift, latency, accuracy, cost, bias, and performing debugging and failure analysis to ensure reliability and business impact

About You

• 1-3+ years of experience in machine learning engineering, software engineering, or a related field, with a track record of deploying models into production
• Passionate about building reliable, scalable ML systems and take ownership of delivering end‑to‑end solutions
• Balance experimentation with engineering rigor, making thoughtful trade‑offs to ensure models are both innovative and production‑ready
• Collaborative problem‑solver who thrives in ambiguous environments and is motivated by delivering measurable business impact

Qualified applicants will receive consideration for employment without regard to their age, race, religion, national origin, ethnicity, gender (including pregnancy, childbirth, etc.), sexual orientation, gender identity or expression, protected veteran status, or disability, in compliance with applicable federal, state, and local legal requirements.

Successful candidates might be required to undergo a background verification with an external vendor.

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2026-04-16 07:15:39