Build and maintain backend services and APIs that power ML-driven features across the Good Inside platform, integrating and orchestrating ML models and third-party APIs into production systems.
Compensation
base salary for this role will be $205k - $235k
Responsibilities
Design, build, and maintain backend services and APIs that power ML-driven features across the Good Inside platform
Integrate and orchestrate ML models and third-party ML APIs into production systems
Build data pipelines and infrastructure to support model serving, feature storage, and real-time personalization
Collaborate closely with product, mobile, and design teams to translate ML capabilities into user-facing features
Own the reliability, performance, and scalability of ML-adjacent backend systems
Requirements
Computer Science degree or equivalent
Nice to have: LLM Application Development
Nice to have: Infrastructure & DevOps Fluency (CI/CD, monitoring, observability)
Nice to have: recommendation systems, personalization engines, content ranking algorithms
Lead data science initiatives within FTI Consulting’s Digital & Insights practice, integrating AI, analytics, and communications strategy to deliver insights and advisory services to global clients.
Responsibilities
Use AI platforms and tools to collect and analyze data
Employ qualitative, statistical and machine learning approaches
Innovate and lead data science teams
Drive projects in audience & influencer insights, media analysis, campaign performance
Interrogate multiple data sources and extract insights
Lead AI Engineer responsible for setting vision, building team, and delivering AI solutions at scale in semiconductor manufacturing.
Compensation
Onto Innovation Inc. offers competitive salaries and a generous benefits package, including health/dental/vision/life/disability, PTO, 401K plan with employer match, and an Employee Stock Purchase Program (ESPP) along with health & wellness initiatives.
Responsibilities
Define AI strategy and architecture for integrating machine learning into core engineering and manufacturing processes.
Partner with tool, process, and applications engineers to map as-is processes and define a to-be AI/automation architecture and deliver measurable outcomes.
Ship agentic assistants for use-cases. Stand up LLM + RAG + tool integrations (via MCP servers) that help engineers accelerate tool operation/setup/maintenance and explain trade-offs, grounded in internal docs, logs, and historical inspection outcomes.
Lead projects across diverse areas: predictive maintenance, computer vision for wafer defect detection, LLM-based engineering assistants, process optimization & yield improvement, simulation and digital twins, build retrieval-augmented AI assistants, architect robust pipelines for data ingestion, labeling, storage, and retrieval across massive multi-modal datasets, stand up scalable MLOps infrastructure, hire, mentor, and manage a team of 3 engineers, work cross-functionally to integrate AI solutions into production environments safely and securely.
AI Research Engineer focused on building systems around research models, defining the AI roadmap, building retrieval, memory, and evaluation systems, and shipping them into production.
Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
Locations (4)
McLean, Virginia, United States
Cambridge, Massachusetts, United States
New York, New York, United States
San Jose, California, United States
Compensation
Cambridge, MA: $197,300 - $225,100 for AI Engineer 4
McLean, VA: $197,300 - $225,100 for AI Engineer 4
New York, NY: $215,200 - $245,600 for AI Engineer 4
San Jose, CA: $215,200 - $245,600 for AI Engineer 4
Responsibilities
Requirements
Ph.D. in machine learning, reinforcement learning, or a related field
Nice to have: Deep expertise in reinforcement learning from human or AI feedback
Nice to have: Reward modeling and credit attribution
Nice to have: Experience training or fine-tuning 30B+ large language models at scale
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