Machine Learning Engineer, AI Studio

This is a fully remote position, open to applicants in United States.

📋 Description

• Take independent ownership of specific production components within enterprise AI products and automation solutions.

• Design, release, diagnose, and support components while linking technical metrics to user and workflow outcomes.

• Establish component boundaries, intended usages, acceptance criteria, non-functional requirements, decision consequences, support expectations, and technical estimates in collaboration with product and architecture teams.

• Design and implement maintainable components using Python, SQL, APIs, data, models, retrieval systems, agent-tools, and workflows with clear contracts, configuration, testing, error handling, and documentation.

• Utilize EDA, feature engineering, supervised or unsupervised methods, baselines, cross-validation, leakage prevention, calibration, subgroup analysis, thresholds, explainability, and error analysis as applicable.

• Develop GenAI, NLP, RAG, and bounded agent components employing structured outputs, embeddings, hybrid search, reranking, provenance, citations, permissions, approvals, retries, and recoverable failure behaviors.

• Create batch or event-driven data, document, feature, embedding, label, and evaluation pipelines with schema validation, lineage, provenance, access control, and consistency checks.

• Define representative evaluation metrics for model quality, uncertainty, retrieval accuracy, grounding, citations, task success, tool correctness, safety, latency, cost, and user impact.

• Release and support components using cloud services, containers, CI/CD, versioning, monitoring, rollback procedures, incident response, and runbooks.

• Lead the diagnosis of moderately complex failures.

• Implement security, privacy, Responsible AI practices, validation, auditability, human oversight, and applicable GxP controls.

• Contribute reusable assets and mentor Associate engineers on familiar tasks.

⛳️ Requirements

• Master’s degree OR Bachelor’s degree with 2 years of experience in Computer Science, IT, or a related field OR Associate’s degree with 6 years of experience in Computer Science, IT, or a related field OR High school diploma/GED with 8 years of experience in Computer Science, IT, or a related field.

• Proven ownership of at least one production software, data, ML, GenAI, or automation component.

• Strong hands-on expertise in Python and SQL, coupled with sound software engineering and testing methodologies.

• Proficient in at least one area of classical ML, GenAI/RAG/agents, or MLOps/platform engineering, along with a working knowledge of related fields.

• Experience with advanced ML and deep learning tools and techniques, including PyTorch, TensorFlow, Hugging Face, scikit-learn, XGBoost, PyMC, computer vision, NLP, GNNs, causal inference, or uncertainty estimation.

• Familiarity with advanced GenAI and knowledge systems, such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, hybrid retrieval, knowledge graphs, graph RAG, or evidence verification.

• Experience with AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, infrastructure as code, MLflow, Airflow, Kubeflow, or GitHub Actions.

• Understanding of MCP-style integration, agent tracing, adversarial testing, durable workflows, permissions, human review, BI, or process automation.

• Experience in healthcare, life sciences, GxP, validated systems, or other regulated or high-impact environments.

• Ability to solve problems independently and exercise sound technical judgment at the component level.

• Clear communication regarding assumptions, evidence, trade-offs, risks, and support implications.

• Strong collaboration skills with business SMEs, product, architecture, software, data, platform, evaluation, and control teams.

• Exhibit ownership, reliability, and disciplined follow-through from design to production support.

• Capability to mentor junior engineers and learn new tools through evidence-based experimentation.

🏝️ Benefits

• A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions.

• Group medical, dental, and vision coverage.

• Life and disability insurance.

• Flexible spending accounts.

• A discretionary annual bonus program.

• Stock-based long-term incentives.

• Award-winning time-off plans.

• Flexible work models where feasible.

• Career development opportunities.

• Work/life balance.

• Financial plans offering opportunities to save for retirement or other goals.