Machine Learning Engineer

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This role is for a Machine Learning Engineer on a W2 contract for over 6 months, offering $114,400 - $135,200 per year. The position is fully remote and requires strong Python and PyTorch skills, experience with ML infrastructure, and the ability to debug complex systems.

Location:

Remote, United States

We are seeking experienced AI/ML Infrastructure Engineers to join a central ML/AI platform team responsible for building foundational services used by multiple internal and external product organizations. This role focuses on designing, extending, and supporting scalable ML infrastructure that powers both traditional machine learning and modern LLM‑based workflows.

• Design, develop, and enhance ML infrastructure services supporting training, inference, experimentation, and embeddings lifecycle management.
• Implement small to medium‑sized features across existing ML platform components.
• Take customer use cases end‑to‑end, including investigation, debugging, and resolution of issues.
• Work across the ML stack, collaborating closely with applied scientists and downstream product teams.
• Diagnose and resolve issues in production ML systems.
• Navigate ambiguous requirements and proactively unblock work by seeking context or collaboration when needed.
• Clearly communicate technical decisions, trade‑offs, and implementation rationale.

• Strong experience with Python, including writing production‑quality, maintainable code.
• Hands‑on experience with PyTorch in real‑world ML systems (training and/or inference).
• Solid understanding of ML fundamentals, including model training vs inference, embeddings and representation learning, and experimentation and evaluation workflows.
• Experience debugging and maintaining complex, distributed systems.
• Ability to reason through problems, explain solutions, and articulate trade‑offs.
• Comfort operating in environments with ambiguity and incomplete requirements.

• Experience building or supporting ML infrastructure platforms.
• Familiarity with feature stores, experimentation frameworks, or inference services.
• Exposure to large‑scale, multi‑team ML environments.
• Prior work supporting both research and production ML use cases.

Note:

Bayside Solutions, Inc. is not able to sponsor any candidates at this time. Candidates must qualify as a W2 candidate.

For applications and inquiries, contact: