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 .

Conduct experiments using in vivo electrophysiology and controlled mouse behavior, collaborate with scientists on behavioral tasks, analyze physiology and behavior data, and train team members in procedures.

Role Description The Union Benefits Compliance Intern will support the HR, Payroll, and Labor Relations teams by helping research and document benefit requirements across multiple unions and trust funds. This role is ideal for a student interested in human resources, labor relations, business administration, or compliance. The intern will gain hands-on experience analyzing Collective Bargaining Agreements (CBAs), Summary Plan Descriptions (SPDs), and trust agreements while helping build a comprehensive database used across the organization. Essential Functions - Research and collect medical and pension benefit information from union CBAs, SPDs, and Trust agreements. - Summarize eligibility rules, contribution requirements, and administrative processes for each union and trust. - Identify and document discrepancies between CBA language and trust-fund requirements. - Maintain accurate records, spreadsheets, and reference materials to support HR, Payroll, and Labor Relations teams. Additional Responsibilities - Performs other duties as assigned. Skills and Abilities - Strong analytical and research skills — able to read, interpret, and summarize written documents such as CBAs, SPDs, and trust agreements. - High attention to detail — ensures accuracy when comparing benefit rules, documenting discrepancies, and maintaining data. - Organizational skills — able to manage multiple documents, track requirements across unions, and maintain structured records and spreadsheets. - Proficiency with Microsoft Excel and basic data tools — comfortable organizing information, creating summaries, and working with tables. - Clear written and verbal communication — able to summarize findings, prepare documentation, and collaborate with HR, Payroll, and Labor Relations teams. - Ability to work independently — comfortable handling research tasks with minimal supervision while meeting deadlines. - Problem-solving mindset — able to identify inconsistencies, flag issues, and think critically about compliance requirements. - Professionalism and confidentiality — able to handle sensitive employee and union information responsibly. Qualifications - Currently pursuing a degree in Human Resources, Business Administration, Labor Relations, Public Policy, or a related field. - Strong attention to detail and ability to interpret written documents. - Interest in compliance, labor relations, or benefits administration. - Proficient in Microsoft Excel and comfortable working with data. - Strong communication and organizational skills. - Ability to work independently and manage multiple tasks. Requirements - This will be a temporary project expected to last approximately two months and can be performed fully remotely. Compensation Information - Pay Type: Hourly - Minimum Pay Range: $18.00 - Maximum Pay Range: $18.00 Benefits - For all Full-time positions only: Ryder offers comprehensive health and welfare benefits, to include medical, prescription, dental, vision, life insurance and disability insurance options. - Paid time off for vacation, illness, bereavement, family and parental leave. - A tax-advantaged 401(k) retirement savings plan.

Role Description We’re hiring Research Engineers to join teams across Meta working at the intersection of frontier AI and real-world product impact. You’ll be embedded directly in Facebook’s ecosystem, helping reimagine core experiences and reshape how people discover content, connect with creators, and interact with each other. The work spans some of the most bold bets in applied GenAI, including: - Building the post-training, evaluation, and serving systems that turn frontier LLMs into reliable, high-quality product experiences used by billions. - Building a general-purpose agentic platform that powers a wide range of GenAI products across Facebook — enabling teams to ship faster, safer, and at scale. - Building systems that enable capacity and cost optimizations through model fine-tuning, post-training and other techniques. - Adapting and scaling these systems across Meta’s products. Why Join Us - Product LLM work at singular scale: Your post-training decisions, evaluation frameworks, and serving architecture directly affect billions of daily interactions. - End-to-end ownership: We don't hand off models to a separate product team. We own the loop from training data to production behavior to measurement. The impact of your work shows up in days, not quarters. - The problems are unsolved: How do you evaluate open-ended conversational AI at scale? How do you fine-tune for groundedness across millions of varied creator profiles? These aren't incremental improvements, they're open research questions with immediate product consequences. - Our team is hands-on, with high autonomy, working on critical bets: We're deliberately keeping this team lean and experienced. You'll have outsized influence on technical direction, not just execution. Depending on your interests and strengths, your work could span: - Post-training pipelines (SFT, RLHF, synthetic data generation) - Evaluation methodology (auto-judge design, benchmark construction, human-AI calibration) - Production serving systems (RAG, memory, multi-modal generation) - Multi-agent orchestration - E2E experience of building agentic products, all grounded in shipping to real users at scale Responsibilities - Contribute to the training of next-generation multimodal foundation models, advance their capabilities in understanding, generation, and grounding, and enable them for downstream product use-cases. - Support creative data sourcing, high-quality pre/mid/post-training data curation, and scale and optimize data pipelines for multimodal large language models (LLMs). - Lead, collaborate, and execute on research that pushes forward the state of the art in multimodal reasoning and generation research, and prioritize research that can be directly applied to Meta’s product development. Qualifications - Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. - Experience as a formal technical lead, leading major technical initiatives with XFN impact, and/or influencing strategy across multiple teams. - Impressive engineering background (PhD in ML not required). - Experience working in AI/ML environments. - Can manage data pipelines and versioning. Benefits - $183,997/year to $257,000/year + bonus + equity + benefits.