Senior Machine Learning Engineer, AI Studio

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

📋 Description

• Define and manage AI assets or significant technical workstreams from problem identification through architecture, development, evaluation, launch, stabilization, support transition, adoption, and measurable outcomes.

• Transform prioritized business needs into governed, reusable AI assets with clear ownership and quantifiable value.

• Identify users, workflows, decisions, intended uses, baselines, value hypotheses, acceptance criteria, adoption paths, operating owners, and measurable outcomes.

• Outline rules, exceptions, data dependencies, and human decision points prior to choosing automation, ML, GenAI, RAG, agents, or manual methods.

• Oversee production architecture encompassing data, feature and knowledge pipelines, models, retrieval, agents, APIs, persistence, workflows, user experience, security zones, and human review.

• Lead the hands-on development of production software, EDA, feature engineering, predictive models, deep-learning or NLP components, inference services, RAG, agent tools, and workflow orchestration.

• Set baselines, design experiments, implement leakage controls, manage uncertainty and calibration checks, conduct subgroup and robustness checks, create gold sets, establish error taxonomies, and set expert adjudication and release thresholds.

• Develop MLOps/LLMOps for lineage, reproducibility, versioning, CI/CD, releases, observability, drift monitoring, SLOs, rollback, incidents, disaster recovery, capacity, cost, and runbooks.

• Coordinate aspects of security, privacy, Responsible AI, quality, legal, model-risk, and GxP controls.

• Create reusable capabilities, assess adoption and value, mentor engineers, and enhance delivery practices.

⛳️ Requirements

• Doctorate degree, or a Master’s degree with 2 years of relevant experience in Computer Science, IT, or related fields, or a Bachelor’s degree with 4 years of experience, or an Associate’s degree with 8 years of experience, or a high school diploma/GED with 10 years of experience.

• Proven end-to-end ownership of at least one production ML, GenAI, software, data, or automation system with measurable results.

• Strong hands-on expertise in Python and SQL.

• Experience in designing production software, services, and evaluation pipelines.

• Advanced skills in Applied ML, GenAI/RAG/agents, or ML platform/MLOps.

• Familiarity with data-centric AI, weak supervision, active learning, conformal or Bayesian uncertainty, causal inference, time-series, survival methods, or drift-aware retraining.

• Experience with transformers, multimodal pipelines, CNNs, RNNs, GNNs, PEFT or LoRA, fine-tuning, distillation, quantization, routing, cascades, or inference optimization.

• Proficiency with AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, serverless systems, infrastructure as code, MLflow, Airflow, Kubeflow, observability, and FinOps.

• Knowledge in human-AI review, correction, approval, accessibility, uncertainty communication, workflow automation, and GxP-relevant or validated systems.

• Strong product mindset and ability to link technical decisions to user needs, workflows, risks, costs, and business value.

• Technical leadership and mentoring capabilities while remaining actively involved in projects.

• Excellent analytical judgment and the ability to communicate evidence, uncertainty, trade-offs, and limitations clearly.

• Cross-functional leadership across business, product, architecture, engineering, and control functions.

• Demonstrated ownership, resilience, and a commitment to continuous improvement through incidents, feedback, and measurable outcomes.

🏝️ Benefits

• A comprehensive employee benefits package.

• Retirement and Savings Plan with generous company contributions.

• Group medical, dental, and vision coverage.

• Life and disability insurance.

• Flexible spending accounts.

• Discretionary annual bonus program.

• Stock-based long-term incentives.

• Award-winning time-off plans.

• Flexible work models where possible.

• Career development opportunities.

• Work/life balance support.