Senior ML Ops Engineer (Machine Learning Infrastructure)

• After 30 Days: You have developed a deep understanding of the product goals, existing infrastructure, and stakeholder requirements. You've conducted technical discovery and proposed a preliminary MLOps architecture—evaluating various ML tools, cloud services, and workflow strategies—clearly outlining pros and cons for each option.
• After 60 Days: You’ve delivered a detailed design document that outlines the end-to-end ML pipeline, including data ingestion, model training, deployment, and monitoring. Based on feedback from ML engineers and stakeholders, you’ve iterated on the design and built PoC for the core ML workflow aligned with the approved architecture.
• After 90 Days: You have delivered the core features of the MLOps pipeline and successfully integrated key tools (e.g., MLflow, SageMaker, or Kubeflow). You’ve also initiated the implementation of the remaining features, ensuring the infrastructure supports scalable, repeatable workflows for model experimentation and deployment in both R&D and production environments.

• Bachelor’s or higher degree in Computer Science, Machine Learning, or a relevant engineering discipline.
• 5+ years of experience building large-scale, reliable systems; 2+ years focused on ML infrastructure or MLOps.
• Proven experience architecting and deploying production-grade ML pipelines and platforms.
• Strong knowledge of ML lifecycle: data ingestion, model training, evaluation, packaging, and deployment.
• Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Airflow, Metaflow, or similar).
• Deep understanding of CI/CD practices applied to ML workflows.
• Proficiency in Python, Git, and system design with solid software engineering fundamentals.
• Experience with cloud platforms (AWS, GCP, or Azure) and designing ML architectures in those environments.

• Experience with deep learning architectures (CNNs, RNNs, Transformers) or computer vision.
• Hands-on experience with distributed training tools (e.g., PyTorch DDP, Horovod, Ray).
• Background in real-time ML systems and batch inference, including CPU/GPU-aware orchestration.
• Previous work in autonomous vehicles, robotics, or other real-time ML-driven systems.

We are committed to providing fair and transparent compensation in accordance with applicable laws.

ranges are listed below and reflect the expected range for new hires in this role, based on factors such as skills, experience, qualifications, and location.

Final compensation may vary and will be determined during the interview process.

The target hiring range for this position is listed below.

Parallel Systems is an equal opportunity employer committed to diversity in the workplace.

All qualified applicants will receive consideration for employment without regard to any discriminatory factor protected by applicable federal, state or local laws.

We work to build an inclusive environment in which all people can come to do their best work.

Parallel Systems is committed to the full inclusion of all qualified individuals.

As part of this commitment, Parallel Systems will ensure that persons with disabilities are provided reasonable accommodations.

If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact your recruiter.