2 months ago
Tempe, AZ, USASenior / Staff+
Base Salary
$140k - $180k/yr
Responsibilities
- Design, develop, train, fine-tune, and evaluate machine learning and transformer-based models for production use cases.
- Build ML training pipelines, experiment tracking workflows, model evaluation frameworks, feature stores, data pipelines, and training-data infrastructure.
- Develop RAG architectures, semantic search pipelines, LLM integrations, prompt-engineering systems, and agentic workflows with tool calling and autonomous task execution.
- Optimize and deploy models and AI services using quantization, batching, caching, containers, Kubernetes, and cloud ML infrastructure.
- Build MLOps capabilities for model versioning, deployment, monitoring, drift detection, feedback loops, and automated retraining.
- Apply AI quality engineering, runtime guardrails, failure taxonomies, evidence-driven release gates, security, privacy, and compliance controls.
- Collaborate with architects, product managers, UX designers, full-stack engineers, and data engineers to ship AI-powered product features.
- Conduct code reviews, mentor engineers, evaluate emerging AI technologies, and contribute to AI architecture reviews and organizational AI maturity initiatives.
Requirements
- Bachelor’s or master’s degree in computer science, machine learning, statistics, mathematics, or a related quantitative field.
- 6–10+ years of professional software engineering experience, including at least 3+ years of production ML/AI engineering experience.
- Expert-level Python proficiency and deep familiarity with the Python ML/AI ecosystem.
- Production PyTorch experience including model definition, custom training loops, autograd, GPU acceleration, and model serialization.
- Experience with Hugging Face Transformers, Datasets, PEFT, foundation-model fine-tuning, RAG pipelines, embeddings, vector stores, and retrieval evaluation.
- Production experience integrating OpenAI, Anthropic, vLLM, or Ollama LLM APIs and building reliable prompt-engineering systems.
- Experience with LangChain or LangGraph for multi-step agent and tool-calling workflows.
- Strong knowledge of supervised and unsupervised learning, loss functions, regularization, evaluation metrics, and statistical validation.
- Experience with MLflow, Weights & Biases, or Comet and reproducible ML workflows.
- Working knowledge of Docker, cloud ML services such as AWS SageMaker, Azure ML, or OCI Data Science, and SQL and NoSQL databases.
- Preferred experience with TensorFlow, JAX, ONNX, OpenCV, model compression, Triton Inference Server, TorchServe, Ray Serve, responsible AI, MCP server development, Kubernetes-based ML orchestration, or relevant domain experience.
- Open-source ML contributions or published research, including papers, patents, or technical blog posts, are preferred.
Benefits
- Hybrid work environment at the Burbank, California headquarters with a flexible remote schedule.
- Health, dental, and vision insurance options.
- 401(k) retirement savings plan with company match.
- Paid holidays, vacation time, and sick time.
- Participation in company equity plans.
- Employee Assistance Program and mental health and wellness programs.
- Training and development opportunities, annual bonus, and merit reviews.
- Access to cloud-based GPU-accelerated compute for model training.
- On-call availability may be required for production AI incidents and model deployment events, with occasional early-morning or evening coordination sessions.
Tech Stack
Categories
About Entertainment Partners | Central Casting
Entertainment Partners provides payroll, residuals, workforce management, and production finance software and services for film, television, and commercial productions, along with guidance on incentives and compliance. Its cloud tools span budgeting, scheduling, payments, and onboarding, and its Central Casting division handles large‑scale background casting. Founded in 1976 and headquartered in Burbank, California, the company is privately held and owned by TPG.