A core technical leadership role bringing state-of-the-art computer vision and multi-modal models from research into production-grade systems for AI-driven game experiences.
Compensation
Base Salary Range: We determine the base salary range for this role based on your primary work location: Zone A: $218,400 - $283,900 Zone B: $194,100 - $252,300 Zone C: $172,200 - $223,900
Responsibilities
Help set the technical vision and roadmap for computer vision and multi-modal AI models
Drive design and implementation of models for image and video understanding, generation, segmentation, detection, and dense prediction
Make sound decisions on model architecture, training strategy, data pipelines, and evaluation
Own the path from research prototype to production: training, fine-tuning, distillation, export, and serving
Collaborate directly with research scientists to translate novel CV and multi-modal model architectures into deployable implementations
Lead the design, governance, and optimization of enterprise-grade AI and data platforms that enable marketing operations and journey activations across omnichannel.
Compensation
The compensation offered for this position will depend on qualifications, experience, and work location. For candidates who receive an offer at the posted level, the starting base salary (for non-sales roles) or base salary + commission target (for sales/com-missioned roles) is expected to be the annual range listed below. The offered compensation may also include restricted stock units and a bonus.
Responsibilities
Define and execute the marketing AI and data platform vision aligned with enterprise and marketing objectives
Lead the development of scalable, secure, and high-performance AI-driven data solutions
Partner with IT and engineering teams to embed and integrate scalable AI
Oversee the design and management of modern data architectures
Partner on architectural design and lead business requirements for enterprise-grade customer data platform
Graduate program in Data & AI and Digital Engineering at bp, involving design, development, and support of digital solutions across cloud platforms and AI technologies.
Compensation
Competitive base salary
One-time sign-on bonus
Annual cash bonus opportunity
Responsibilities
Design, build, automate, and maintain secure, reliable, and scalable cloud-based solutions using technologies across AWS and Azure.
Develop software applications, services, and APIs using modern programming languages.
Participate in web and mobile application development using modern frameworks and development practices.
Support the implementation of engineering best practices, including agile delivery, automation, testing, observability, security, and reliability.
Collaborate with business subject matter experts, product teams, data engineers, data scientists, analysts, and platform engineers to solve complex business challenges.
A corporate career development program designed for Seattle College Juniors (2027 graduates) with technology majors to expand their knowledge of EG, experience professional development through mentorship, and gain valuable interview practice.
Responsibilities
Develop technical and application knowledge
Immersed in a supportive culture
Build connections with technology teams
Partnered with a mentor
Explore software development engineering roles such as general software development engineering, data engineering, machine learning engineering, and security engineering
As a Senior Machine Learning Engineer at Iterable, you will design and build end-to-end machine learning systems that power intelligence across our product.
Responsibilities
Build and ship core product ML capabilities including send-time/frequency optimization, recommendation engines, and campaign personalization.
Own the full model lifecycle end-to-end: EDA, feature engineering, distributed training, production deployment, latency monitoring, and online experimentation (e.g., multi-armed bandits, ensemble models).
Build and scale our data pipelines and serving architectures using Databricks, Spark, AWS, Ray, and Kubernetes.
Partner with Backend and Platform teams to elevate feature serving, improve overall data architecture, and establish engineering standards across the team.
Mentor engineers on the team and help set the technical direction for ML at Iterable.
Machine learning engineer leading the design and build of an agentic AI system to automatically resolve insurance claim denials for healthcare practices.
Compensation
The base pay range for this role is $150,000–$275,000 per year.
Responsibilities
Design and build the architecture for our agentic AI system that autonomously resolves insurance claim denials
Develop specialized AI agents for denial classification, root cause analysis, evidence retrieval, policy reasoning, and appeal generation
Implement multi-agent orchestration frameworks that coordinate complex workflows across research, decision-making, and document generation
Build and optimize RAG systems to retrieve relevant clinical documentation, billing records, and payer policy information
Create evaluation frameworks and feedback loops to continuously improve agent performance and reliability
Lead end‑to‑end deployments of AI models in life sciences organizations, designing production systems and ensuring regulatory compliance.
Responsibilities
Design and ship production systems around models, owning integrations, data provenance, reliability, and on‑call readiness across research, clinical, and operational workflows.
Lead discovery and scoping from pre‑sales through post‑sales, translating ambiguous workflow needs into hypothesis‑driven problem framing, system requirements, and an execution plan with measurable endpoints.
Define and enforce launch criteria for regulated contexts, including validation evidence, audit readiness, outcome metrics, and drive delivery until sustained production impact is demonstrated.
Build in sensitive scientific data environments where auditability, validation, and access controls shape architecture, operating procedures, and failure handling.
Run evaluation loops that measure model and system quality against workflow‑specific scientific benchmarks and use results to drive model and product changes.
Requirements
PhD
MS
equivalent applied experience in a life sciences relevant field
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