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On Call Remote Computer Engineer Jobs (NOW HIRING)

May provide customer support for on-call help desk during certain periods during the day. No option ... Bachelor's degree in Computer Science, information systems, computer engineering or related field ...

UI/UX SME

Crane, IN · On-site +1

$185K/yr

Computer Engineer, UI/UX and Dashboard Development We are seeking a senior Computer Engineer IV ... This position can be fully remote , and any required travel would be reimbursed by the government

$109K - $129K/yr

Approval of remote and hybrid work is not guaranteed regardless of work location.For additional ... AND POSITION REQUIREMENTS The Penn State Engineering Librarylocatedin the ECoRE Building on the ...

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On Call Remote Computer Engineer information

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How much do on call remote computer engineer jobs pay per year?

As of Aug 12, 2026, the average yearly pay for on call remote computer engineer in the United States is $121,515.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,500.00 and $131,500.00 per year, depending on experience, location, and employer.
What are the most commonly searched types of Remote Computer Engineer jobs? The most popular types of Remote Computer Engineer jobs are:
Infographic showing various On Call Remote Computer Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, 3% Contract, and 1% Nights. Highlights an 80% Physical, 1% Hybrid, and 19% Remote job distribution, with an average salary of $121,515 per year, or $58.4 per hour.

Computational Biology MLOps Engineer (Remote)

Marlabs

San Diego, CA • Remote

$114K - $134K/yr

Full-time

Posted 19 days ago


Job description

Marlabs, a global AI and Digital Solutions Consulting firm, delivers intelligent solutions across AI, data, analytics, and product engineering. Since 2000, we have partnered with some of the largest healthcare, life sciences, financial services, and government organizations worldwide. As we continue to expand our global footprint, we have an exciting opportunity for a highly skilled Computational Biology MLOps Engineer to join our innovative and dynamic team.

This role requires onsite work three (3) days per week in Indianapolis, IN or San Diego, CA.

Computational Biology MLOps Engineer | About You

As a Computational Biology MLOps Engineer, you are responsible for building and scaling the ML infrastructure that supports next generation in silico protein design and engineering. You bridge cutting edge AI research and production systems at the intersection of machine learning, computational biology, and high performance computing. You thrive in cross functional environments and partner closely with computational scientists and platform engineers to accelerate research velocity. You bring strong software, DevOps, and data engineering fundamentals with hands on experience across CI/CD, orchestration, and distributed training. Experience working with scientific or multimodal data and interest in protein language and generative models is a plus.

Computational Biology MLOps Engineer | Day-to-Day

  • Build and maintain ML infrastructure, including CI/CD pipelines (GitHub Actions) for model training, evaluation, and deployment.
  • Orchestrate compute across Kubernetes clusters and SLURM and HPC environments to optimize utilization for large scale training.
  • Develop robust and scalable data pipelines that deliver ML ready datasets from biological sources such as PDB and mmCIF files, sequence databases, and assay readouts.
  • Create tools and frameworks that enable rapid iteration on protein language models, diffusion models, and other generative approaches.
  • Architect systems that scale across distributed environments and support multimodal datasets for large foundational models.
  • Implement monitoring, logging, and alerting to ensure reliability, performance, and cost efficiency of production ML systems.

Computational Biology MLOps Engineer | Skills & Experience

  • 5+ years of overall industry experience in software engineering, DevOps, data engineering, or ML engineering roles, including 3+ years of focused MLOps experience building and maintaining production grade ML infrastructure.
  • Proven CI/CD expertise with GitHub Actions and strong DevOps practices including infrastructure as code, version control, and collaborative workflows.
  • Hands on Kubernetes experience in deploying and managing containerized ML workloads with familiarity using container registries.
  • Proficiency with SLURM or similar job schedulers in HPC environments and experience with distributed training optimization including mixed precision and checkpointing.
  • Strong Python skills and experience with major ML frameworks including PyTorch, TensorFlow, or JAX.
  • Experience building ETL processes and scalable data and feature pipelines and experience with cloud platforms such as AWS, GCP, or Azure.
  • Preferred experience with scientific data, protein structure formats such as PDB and mmCIF, protein AI models including ESM, and agentic systems such as MCP, LangGraph, and LangChain.