Role Description We are seeking an AI Research Engineer to bridge cutting-edge applied research and production engineering, designing and shipping advanced machine learning systems that solve high-impact business problems. The role blends scientific rigor with practical software engineering, requiring deep understanding of modern ML and deep learning techniques alongside the ability to build robust, scalable, and well-instrumented production pipelines. The ideal candidate stays current with the rapidly evolving AI research landscape, can critically evaluate new techniques for real-world applicability, and is comfortable operating across the full lifecycle from problem framing and experimentation to deployment and continuous improvement. Key Responsibilities - Design, prototype, and evaluate applied AI solutions across natural language, vision, recommendation, and structured data domains. - Translate ambiguous business problems into well-scoped ML formulations with clear success metrics and evaluation strategies. - Stay current with the latest research in deep learning, large language models, and adjacent areas, and assess applicability to internal use cases. - Implement rigorous experimentation workflows including baselines, ablations, and statistically sound evaluation methodology. - Build production-quality training and inference pipelines using modern ML frameworks and orchestration tools. - Collaborate with ML platform engineers to ensure efficient use of compute, storage, and accelerator resources. - Optimize models for accuracy, latency, throughput, and cost based on production requirements. - Develop tooling for dataset construction, labeling, validation, and ongoing monitoring of data quality. - Partner with product, design, and domain experts to ensure model behavior aligns with user needs and policy requirements. - Implement safety, fairness, and reliability evaluations and incorporate findings into model selection decisions. - Document research findings, design decisions, and operational characteristics clearly for both technical and non-technical audiences. - Mentor engineers on applied ML methodology, evaluation rigor, and responsible deployment. - Contribute to internal knowledge sharing, reading groups, and prototype-to-production playbooks. - Influence the broader AI roadmap based on research insight, capability gaps, and emerging opportunities. Qualifications - Master’s or PhD in Computer Science, Machine Learning, Statistics, or a closely related field; or equivalent applied experience. - Six or more years of combined research and applied ML engineering experience. - Strong proficiency in Python and modern ML frameworks such as PyTorch or JAX. - Hands-on experience training, fine-tuning, and evaluating deep learning models at non-trivial scale. - Solid grounding in mathematics, statistics, and the theoretical foundations of modern ML. - Experience taking ML models from research prototype to production with appropriate observability and safeguards. - Familiarity with distributed training, mixed-precision training, and accelerator hardware. - Strong written and verbal communication skills, including ability to explain complex methods clearly. - Demonstrated ability to read, evaluate, and adapt techniques from current research literature. - Track record of shipping impactful applied AI projects. Preferred Qualifications - Published research at top-tier AI/ML venues. - Experience with large language model training, fine-tuning, or evaluation. - Familiarity with retrieval-augmented generation, agentic systems, or multimodal architectures. - Exposure to responsible AI, model evaluation, and alignment practices. - Experience contributing to open-source ML projects. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] or contact us at (908) 505-3544. Learn more about Bright Vision Technologies at www.bvteck.com . Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

Role Description We are seeking a Machine Learning Data Engineer to build and operate the large-scale data systems that power modern AI training and evaluation pipelines. The role combines deep data engineering expertise with a strong understanding of AI workloads, focusing on: - Ingestion - Transformation - Quality assurance - Lineage - High-throughput delivery of data to training jobs across diverse modalities The ideal candidate has experience operating petabyte-scale data systems, strong software engineering fundamentals, and a clear understanding of how data infrastructure choices propagate into model quality and training efficiency. Qualifications - Bachelor’s or Master’s degree in Computer Science or a related field - Six or more years of data engineering experience, with significant work supporting ML or AI workloads - Strong proficiency in Python and at least one JVM or systems language - Deep experience with modern data processing frameworks such as Spark, Ray, or Beam - Hands-on experience operating petabyte-scale storage and pipeline systems - Strong understanding of distributed systems, data modeling, and storage formats - Experience with dataset versioning, lineage, and reproducibility for ML workflows - Familiarity with high-throughput data loading for accelerator-based training - Strong software engineering practices including testing, CI/CD, and code review - Excellent communication and cross-functional collaboration skills Requirements - Experience with multimodal datasets at large scale - Familiarity with data quality tooling and dataset evaluation methodology - Exposure to privacy-preserving data systems and regulated data handling - Open-source contributions to data infrastructure projects - Experience supporting frontier model training pipelines Benefits - 100% Remote (U.S.) - Full-time, Direct W2 - Salary Range: $100,000–$150,000 Annually How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] . Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

Working at Atlassian Atlassians can choose where they work - whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity. Interviews and onboarding are conducted virtually, a part of being a distributed-first company. As a Principal Machine Learning engineer, you will drive the development and implementation of the cutting edge machine learning algorithms, training sophisticated models, collaborating with product, engineering, and analytics teams, to build the AI functionalities into each Atlassian products and services. Your daily responsibilities will encompass a broad spectrum of tasks such as designing system and model architectures, conducting rigorous experimentation and model evaluations, and providing guidance to emerging ML engineers. Your role is pivotal, stretching beyond these tasks, ensuring AI's transformative potential is realized across our offerings. On the first day, we'll expect you to have - 10+ years of total experience, 5+ years of related industry experience in the MLE / data science domain - Fluency in Python - Solid understanding of machine learning concepts and algorithms, including supervised and unsupervised learning, deep learning, and NLP. - Familiarity with popular ML libraries like sci-kit-learn, Keras/TensorFlow/PyTorch, numpy, pandas - Good Understanding of Machine Learning project lifecycle - Experience in architecting and implementing high-performance RESTful microservices ( API development for ML Models ) - Familiarity with MLOps and experience with scaling and deploying Machine Learning models - Focus on business practicality and the 80/20 rule; very high bar for output quality, but recognize the business benefit of "having something now" vs "perfection sometime in the future" - Agile development mindset, appreciating the benefit of constant iteration and improvement It's Great, But Not Required, If You Have - Experience in developing deep learning-based models and working on LLM-related applications - Excelling in solving ambiguous and complex problems, being able to navigate through uncertain situations, breaking down complex challenges into manageable components and developing innovative solutions. - Experience or passion on building cutting edge developer tools products with AI/ML At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range. This role may also be eligible for benefits, bonuses, commissions, and equity. Benefits & Perks Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits . About Atlassian At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together. We believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines. To provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them. To learn more about our culture and hiring process, visit go.atlassian.com/crh . In line with local law, identity verification (which may include use of biometric data) is a condition of employment with Atlassian for employment fraud purposes.

• Independently own high-value optimization initiatives across training, inference, or launch-readiness for important Ads ML workloads. • Diagnose bottlenecks in real production systems using profiling, benchmarking, and observability rather than intuition-first debugging. • Build performance tooling, optimization playbooks, observability hooks, guardrails, or efficiency primitives that help more than one team or workload over time. • Improve launch-safety and efficiency readiness by contributing to load testing, fallback readiness, latency and cost visibility, and operational confidence for heavy models. • Work with model owners and platform teams to land pragmatic fixes while helping the team gradually standardize repeated solutions. • Contribute to the team’s technical direction by surfacing patterns, tradeoffs, and opportunities for reuse or automation. • Mentor less-experienced engineers through code, debugging, measurement rigor, and strong execution habits.