Environment Modeller Job at Weta Digital (Wellington)

Avp ml and genai model validation

Embark on a transformative journey as an AVP ML and GenAI Model Validation at Ba...

Location

India , Noida

Salary:

Not provided

Barclays

Expiration Date

Until further notice

Requirements

Highly numerate, evidenced by an Honours degree, master’s, or PhD (or equivalent) in a quantitative discipline such as Computer Science, Mathematics, Physics, Operational Research, Economics, Engineering, or Finance
Experience developing, validating, or independently reviewing best‑in‑class tools and AI/ML models for large financial institutions or comparable regulated environments, with the ability to effectively challenge and improve model methodologies and model management processes
Strong analytical and technical expertise in AI/ML modelling, including experience with ML algorithms (e.g., XGBoost, LightGBM, Random Forest), NLP models, and deep learning frameworks (e.g., TensorFlow, PyTorch, Keras)
Experience developing or validating AI/ML models on large datasets in Hadoop/AWS environments
Designed, fine‑tuned, and shipped Generative AI models - transformer‑based LLMs, RAG systems for grounded retrieval, and GANs-using PyTorch/TensorFlow and Hugging Face, including prompt engineering, RLHF, and rigorous evaluation of GenAI models
Hands‑on experience with Generative AI technologies, including Large Language Models, Transformers, retrieval‑augmented generation (RAG), and prompt‑based systems
familiarity with LangChain and LangGraph
Experience designing, developing, validating, or reviewing agent‑based AI architectures, including multi‑agent orchestration, task planning, memory management, and safeguard mechanisms
Sound understanding of financial services regulatory expectations and model risk guidance (e.g., SR 11‑7, SS1/23, IFRS 9, EU AI Act etc.)
Strong communication and influencing skills, with the ability to produce high‑quality written material for both technical and senior non‑technical audiences

Job Responsibility

Validation of models for their intended use and scope, commensurate with the complexity and materiality of the models
Approval or rejection of a model or usage based on assessment of the model’s conceptual soundness, performance under intended use and the clarity of the documentation of the model’s inherent risks, limitations and weaknesses
Assessment of any compensating controls used to mitigate Model risk
Documentation of validation findings and recommendations in clear and concise reports, providing actionable insights for model improvement
Evaluation of the coherence of model interactions and quality of Large Model Framework aggregate results that generate output for regulatory submissions or management decision making and planning
Design of the framework and methodology to measure and, where possible, quantify model risk, including the assessment of framework level uncertainty

What we offer

Hybrid working
Modern workspaces, collaborative areas, and state-of-the-art meeting rooms
Facilities include wellness rooms, on-site cafeterias, fitness centers, and tech-equipped workstations

Fulltime

Technical Program Manager - Pre-silicon Team

We are seeking a Technical Program Manager to coordinate the development and qua...

Location

United States , Austin

Salary:

171200.00 - 256800.00 USD / Year

AMD

Expiration Date

Until further notice

Requirements

Use of AI tools for planning, development and debug
Familiarity with pre-silicon methodologies, software modeling, emulation, and hybrid modeling environments
Program Management experience with software development and release lifecycle
Skilled in collaboration tools: Confluence, JIRA and MS Office suite (Excel, PowerPoint, Project)
Knowledge of x86 platform architecture and debug
Excellent communication, management, and presentation skills
Adept at collaboration among top-thinkers and senior architects with strong interpersonal skills to work across teams in different geographies
Bachelor’s or Master’s degree in related discipline preferred

Job Responsibility

Define, plan, and drive SimNow development and model qualification involving multi-component environments (AMD IPs, SoCs, HW, SW, FW)
Comprehend the SOC as a complete system which includes HW (Silicon), FW, BIOS & SW and ensure that SimNow models are aligned to enable pre-silicon feature enablement plans
Develop and improve processes for project tracking and risk mitigation
Track and report status vs. objectives throughout the lifecycle
Liaison between teams to implement latest methodology and shift-left initiatives
Communicate progress and conduct regular reviews with AMD senior management and customers
Raise red flags proactively when risks emerge and partner with technical teams to resolve issues
Drive resource planning for development, qualification, and support
Manage dependencies with design teams for SimNow model development
Craft, lead, and continually improve program-level processes and methodologies

Fulltime

Principal Engineer

The Principal AI/ML Operations Engineer leads the architecture, automation, and ...

Location

United States , Pleasanton, California

Salary:

251000.00 - 314500.00 USD / Year

BlackLine

Expiration Date

Until further notice

Requirements

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field
10+ years in ML infrastructure, DevOps, and software system architecture
4+ years in leading MLOps or AI Ops platforms
Strong programming skills in languages such as Python, Java, or Scala
Expertise in ML frameworks (TensorFlow, PyTorch, scikit-learn) and orchestration tools (Airflow, Kubeflow, Vertex AI, MLflow)
Proven experience operating production pipelines for ML and LLM-based systems across cloud ecosystems (GCP, AWS, Azure)
Deep familiarity with LangChain, LangGraph, ADK or similar agentic system runtime management
Strong competencies in CI/CD, IaC, and DevSecOps pipelines integrating testing, compliance, and deployment automation
Hands-on with observability stacks (Prometheus, Grafana, Newrelic) for model and agent performance tracking
Understanding of governance frameworks for Responsible AI, auditability, and cost metering across training and inference workloads

Job Responsibility

Define enterprise-level standards and reference architectures for ML-Ops and AIOps systems
Partner with data science, security, and product teams to set evaluation and governance standards (Guardrails, Bias, Drift, Latency SLAs)
Mentor senior engineers and drive design reviews for ML pipelines, model registries, and agentic runtime environments
Lead incident response and reliability strategies for ML/AI systems
Lead the deployment of AI models and systems in various environments
Collaborate with development teams to integrate AI solutions into existing workflows and applications
Ensure seamless integration with different platforms and technologies
Define and manage MCP Registry for agentic component onboarding, lifecycle versioning, and dependency governance
Build CI/CD pipelines automating LLM agent deployment, policy validation, and prompt evaluation of workflows
Develop and operationalize experimentation frameworks for agent evaluations, scenario regression, and performance analytics

What we offer

short-term and long-term incentive programs
robust offering of benefit and wellness plans

Fulltime

Senior Principal Machine Learning Engineer - LLM Post-Training and Optimization

Atlassian is seeking a highly skilled and experienced Senior Principle Machine L...

Location

United States , Mountain View

Salary:

243100.00 - 407200.00 USD / Year

Atlassian

Expiration Date

Until further notice

Requirements

Ph.D. or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field
8+ years of experience in machine learning, with a focus on large-scale model development and optimization
Deep expertise in LLM and transformer architectures (e.g., GPT, BERT, T5)
Strong proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow
Experience with distributed training techniques and large-scale data processing pipelines
Proven track record of deploying machine learning models in production environments
Familiarity with model optimization techniques, including quantization, pruning, and knowledge distillation
Strong problem-solving skills and ability to work in a fast-paced, collaborative environment
Excellent communication skills and ability to translate technical concepts for diverse audiences

Job Responsibility

Lead the fine-tuning and post-training optimization of large language models (LLMs) for diverse applications
Develop and implement techniques for model compression, quantization, pruning, and knowledge distillation to optimize performance and reduce computational costs
Conduct research on advanced techniques in transfer learning, reinforcement learning, and prompt engineering for LLMs
Design and execute rigorous benchmarking and evaluation frameworks to assess model performance across multiple dimensions
Collaborate with infrastructure teams to optimize LLM deployment pipelines, ensuring scalability and efficiency in production environments
Stay at the forefront of advancements in LLM technologies, sharing insights, driving innovation within the team, and leading agile development
Mentoring other team members, facilitating within/across team workshops, fostering a culture of technical excellence and continuous learning

What we offer

health coverage
paid volunteer days
wellness resources

Fulltime

Staff Machine Learning Engineer

As a Staff Machine Learning Engineer at Aignostics, you will play a crucial role...

Location

Germany , Berlin

Salary:

Not provided

Aignostics

Expiration Date

Until further notice

Requirements

Advanced degree in a relevant field or extensive work experience
8+ years of industry experience, with at least 2 years as Staff Engineer or an equivalent role
Proven track record of driving technical excellence and innovation
Solid background in data-intensive systems and software architecture, design patterns and clean coding
Expert Python programming and fluency in C/C++ or other low-level language(s)
Experience with designing and implementing large-scale, distributed ML systems and platforms
Proven track record of deploying ML models into production environments
Strong knowledge of machine learning fundamentals
Experience with deep learning frameworks (e.g. Pytorch and Tensorflow) and state-of-the-art techniques (e.g. generative models)
Deep understanding of cloud technologies (e.g. GCP, AWS), containerization and orchestration (Kubernetes)

Job Responsibility

Define and drive the technical architecture and system design principles for our AI platform and infrastructure
Work in close collaboration with engineering leads to build flexible frameworks and systems for model training, evaluation and inference across different pathology applications
Guide the CTO office, product management and fellow engineering leads through complex decisions by providing expert consultation on feasibility, architecture, trade-offs and risk mitigation strategies, while ensuring alignment with our technical vision
Foster technical alignment across teams by establishing shared architectural principles and best practices, facilitating cross-team design reviews to enable consistent decision-making across domains
Champion technical excellence by leading strategic initiatives that modernize our architecture and reduce technical debt while measuring and improving our technical health metrics
Elevate the technical capabilities of our engineering staff through structured mentoring, workshops and establishing comprehensive technical guidelines that enable teams to make better design decisions
Drive innovation by evaluating emerging technologies, leading proof-of-concept initiatives and building support for strategic technical investments that advance our engineering capabilities while ensuring measurable business value

What we offer

Cutting-edge AI research and development, with involvement of Charité, TU Berlin and our other partners
Work with a welcoming, diverse and highly international team of colleagues
Opportunity to take responsibility and grow your role within the startup
Expand your skills by benefitting from our Learning & Development yearly budget of 1,000 € (plus 2 L&D days), language classes and internal development programs
Mentoring program, you’ll learn from great experts
Flexible working hours and teleworking policy
30 paid vacations days per year
We are family & pet friendly and support flexible parental leave options
Pick a subsidized membership of your choice among public transport, sports and well-being
Enjoy our social gatherings, lunches and off-site events for a fun and inclusive work environment