Senior ML Engineer — Production-Grade AI Systems (Hybrid)

bp; Houston (TX); Hybrid; USD 140,000 - 170,000; Full time

Benefits offered by this job

Hybrid working arrangements

Competitive compensation and benefits

Job summary

bp in Houston is seeking a Senior Machine Learning Engineer who blends deep ML expertise with strong software engineering to design, build, and deploy production-grade ML systems. You will apply ML as a core discipline, delivering scalable AI products across NLP, optimisation, simulation, or generative AI.

You’ll collaborate with data scientists, data engineers, software engineers, and domain experts to translate complex problems into deployable ML products while following CI/CD, testing,

Qualifications

• Advanced degree in a quantitative field (e.g., CS, Math, Physics, Engineering).
• 5+ years designing, prototyping, productionising ML systems in sophisticated environments.
• Strong expertise in ML algorithms, modelling, and optimisation for production.

Responsibilities

• Design, build, and maintain scalable, production-grade ML systems and pipelines.
• Apply ML to develop novel algorithms and models deployed as reliable products.
• Build ML products across NLP, optimisation, and AI techniques.
• Translate problems into deployable ML solutions with actionable insights.
• Collaborate with data scientists, software engineers, and domain experts.

Skills

Python

Machine learning

MLOps

CI/CD

SQL

Distributed systems

Software engineering

Go

C++

Java

Education

MSc or PhD in a quantitative field

Tools

Hadoop

Spark

Kubernetes

Docker

Job description

bp in Houston is seeking a Senior Machine Learning Engineer who blends deep ML expertise with strong software engineering to design, build, and deploy production-grade ML systems. You will apply ML as a core discipline, delivering scalable AI products across NLP, optimisation, simulation, or generative AI.

You’ll collaborate with data scientists, data engineers, software engineers, and domain experts to translate complex problems into deployable ML products while following CI/CD, testing,