Machine Learning Engineer, AI Studio

Scorpion Therapeutics; United States; On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Total Rewards Plan

Discretionary annual bonus

Stock-based incentives

Flexible work models

Job summary

Scorpion Therapeutics is seeking a Machine Learning Engineer for its AI Studio to own production components spanning model services, data pipelines, and retrieval systems. You will design and deploy components that connect ML initiatives to real-world user workflows, ensuring reliability and measurable impact.

Applicants should demonstrate expertise in Python/SQL, cloud-based ML tooling, and end-to-end delivery, including testing, monitoring, and governance practices.

Qualifications

• Master’s degree or Bachelor’s degree + relevant experience
• Experience with ML/GenAI techniques and model deployment

Responsibilities

• Own defined production components within enterprise AI products and automation solutions.
• Design, build, release, diagnose, and support components that connect technical measures to user and workflow outcomes.
• Contribute to governed, reusable AI assets across the AI lifecycle from discovery to production.

Skills

Python

SQL

APIs

Data modeling

ML/GenAI techniques

Education

Master’s degree

Tools

AWS Bedrock/SageMaker

Databricks/Spark

Kubernetes

MLflow

Airflow/Kubeflow/GitHub Actions

Job description

Machine Learning Engineer, AI Studio

What You Will Do

• Independently own defined production components within enterprise AI products and automation solutions.
• Design, build, release, diagnose, and support components that connect technical measures to user and workflow outcomes.
• Contribute to governed, reusable AI assets across the AI lifecycle (discovery/prototyping to production, reuse, and measurable business impact).
• Components may include: model/inference service, data/knowledge pipeline, retrieval, agent tools, evaluation module, APIs, workflows, and monitoring.

Key Responsibilities

• Define component boundaries, intended use, acceptance criteria, non-functional requirements, decision consequences, support expectations, and technical estimates.
• Design and implement maintainable Python/SQL/API/data/model/retrieval/agent-tool/workflow components with contracts, configuration, testing, error handling, and documentation.
• Apply appropriate ML/GenAI techniques (EDA, feature engineering, supervised/unsupervised, baselines, cross-validation, leakage prevention, calibration, subgroup, explainability, error analysis).
• Build GenAI/NLP/RAG/bounded agent components (structured output, embeddings, hybrid search, reranking, provenance/citations, permissions/approvals, retries, recoverable failures).
• Engineer batch/event-driven pipelines with schema validation, lineage/provenance, access control, and consistency checks.
• Define evaluation covering quality, uncertainty, retrieval grounding, citations, task success, tool correctness, safety, latency, cost, and user impact.
• Release/support via cloud, containers, CI/CD, versioning, monitoring, rollback, incident response, and runbooks.
• Apply security, privacy, Responsible AI, validation, auditability, human oversight, and applicable GxP controls; contribute reusable assets and guide associates.

Basic Qualifications

• Master’s degree; or Bachelor’s degree + 2 years CS/IT/related; or Associate’s degree + 6 years; or High school/GED + 8 years.

Preferred Qualifications (select)

• Production AI/ML system design in Python/SQL; APIs/background jobs/event flows; testing/performance/observability.
• GenAI/RAG/agents (structured output, embeddings, hybrid retrieval, reranking, citations, access-aware retrieval, tool schemas, human approval).
• Cloud and MLOps (AWS, Bedrock/SageMaker, Databricks/Spark, Kubernetes, IaC, MLflow, Airflow/Kubeflow/GitHub Actions).
• Advanced ML/deep learning (e.g., PyTorch/TensorFlow, Hugging Face, scikit-learn, uncertainty).
• Regulated enterprise delivery (healthcare/life sciences/GxP).

Benefits

• Total Rewards Plan (eligibility-based): health/welfare, retirement/savings, work/life balance, career development; employee benefits package, discretionary annual bonus (or sales incentive), stock-based long-term incentives, award-winning time-off, and flexible work models where possible.