Machine Learning Engineer (AI)

Location: United States, with locations listed in Thousand Oaks, California, and Washington, D.C.

Company

hirify.global is a biotechnology company developing and delivering medicines for serious illnesses across oncology, inflammation, general medicine, and rare disease.

What you will do

• Design, build, release, diagnose, and support production components for enterprise AI products and automation solutions.
• Develop maintainable Python, SQL, API, data, model, retrieval, agent-tool, and workflow components with testing, documentation, and clear contracts.
• Build GenAI, NLP, RAG, bounded-agent, knowledge, embedding, and evaluation components with provenance, citations, permissions, and recoverable failure handling.
• Engineer batch and event-driven data, document, feature, embedding, label, and evaluation pipelines with validation, lineage, access control, and consistency checks.
• Define evaluation for model quality, retrieval, grounding, safety, latency, cost, task success, and user impact.
• Deploy and operate components using cloud services, containers, CI/CD, monitoring, rollback procedures, incident response, and runbooks; guide associate engineers.

Requirements

• Master’s degree, or a bachelor’s degree with 2 years of relevant experience, or an associate degree with 6 years of relevant experience, or a high school diploma/GED with 8 years of relevant experience.
• Strong hands-on proficiency in Python and SQL, with sound software-engineering and testing practices.
• Experience with production AI/ML system design, APIs, background jobs, event flows, performance, observability, source control, and maintainable failure semantics.
• Capability in at least one of classical machine learning, GenAI/RAG/agents, or MLOps/platform engineering, with working knowledge of adjacent areas.
• Experience with data and cloud-scale systems such as pipelines, relational/document/graph/vector stores, containers, Spark or Databricks, and cloud-native services.
• Ability to communicate technical assumptions, evidence, trade-offs, risks, and support implications while collaborating with business, product, architecture, software, data, platform, evaluation, and control partners.

Nice to have

• Experience with PyTorch, TensorFlow, Hugging Face, scikit-learn, XGBoost, PyMC, computer vision, NLP, GNNs, causal inference, or uncertainty estimation.
• Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, hybrid retrieval, knowledge graphs, graph RAG, or evidence verification.
• Experience with AWS, Bedrock, SageMaker, Databricks, Spark, Kubernetes, infrastructure as code, MLflow, Airflow, Kubeflow, or GitHub Actions.
• Experience in healthcare, life sciences, GxP, validated systems, or another regulated or high-impact environment.

Culture & Benefits

• Collaborative, innovative, and science-based work environment.
• Health, dental, and vision coverage, life and disability insurance, and flexible spending accounts.
• Retirement and savings plan with company contributions, stock-based long-term incentives, and a discretionary annual bonus program.
• Award-winning time-off plans and career development opportunities.
• Flexible work models where applicable to the posted work location type.