Role Description The Software Development Engineer in Test (SDET) ATM, POS and Card apps Labs for RBC Caribbean Technology, will be responsible for testing and automation processes, methodologies, test strategy and improvements of our overall test delivery. You are accountable for driving quality by leveraging automation to improve quality through early defect detection. You will be responsible for creating test automation scripts for ATM, POS and Card issuing and acquiring Apps using existing and new automation frameworks and tools. You will be accountable for maximizing automation test coverage and using business analysis and automation techniques to optimize testing activities. - Responsible for testing of ATM, point of sale terminals, integration to cards and payment systems, including debit and credit transaction processing, settlement and reconciliation reports. - Accountable for driving Automation and automated test case creation and execution, maximizing automation for all project deliverables and common quality activities. - Responsible for developing and executing automated test plans for ecosystem/program/large projects (might assume planning execution accountability). - Accountable for driving quality and champion defect prevention/early defect detection. - Responsible for designing, building, or enhancing scalable and robust BDD based automation framework and automated test cases. - Responsible for modelling system behaviors/attributes performing model optimization to improve scope and functional coverage and maintain models as business requirements change. - Create test suites, traceability matrix etc. and provide support on test suite walkthrough with different teams. - Develop and maintain existing applications automation scripts for test activities and aligning to RBC Quality Engineering practices while partnering across IT and with assigned business lines to plan functional and non-functional testing activities. - Responsible for all test execution and maximizing automation for all project deliverables and for following defined processes and tools and alignment with overall QE strategy and framework for the applications. - Effectively communicates and build automation reports and metrics for project stakeholders and business partners from initiation to close. - Promotes collaboration and partnering to develop and meet core project objectives and provides coaching to team members on Automation framework, DevOps and applicable processes, practices, and tools. - Look outside for opportunities to disrupt from within and continually expand what is possible through technology. - Work within a cross functional team aimed at delivering high quality solutions. - Code, test and implement full stack solutions to meet business needs. - Create intuitive, robust, and reusable test and automation interfaces using modern frameworks and help build strong automation regression suites and ensure quality deliverables. Qualifications - Computer Engineering, Computer Science or related (technical) degree/diploma. - Typically requires 2 to 4 years of hands-on active software engineering and test automation coding experience. - Demonstrated experience with ATM, point of sale terminals, cards and payment and other banking systems testing. - 1-2 years of experience in testing Web Applications and File validations with proven knowledge of API and Database testing and test automation and rich experience in different types of testing. - Banking functionality knowledge. - Experience in test case design at different levels (component/service/APIs, integration, mobile, end to end/user scenarios) or non-functional testing (depending on role). - Proven experience including development and defining & owning test automation infrastructure for a large enterprise. - Technical Knowledge in ISO8583, NCR APTRA, IBM MQ, Python, Java, BDD, Cucumber Framework (Selenium/UFT/Lean UFT, RestAssured, Robot Framework, TestNG, Jenkins), Microservices and APIs (WireMock, postman, swagger), build tools such as Maven/Gradle and Source Code Management tool such as Git and GitHub. - Hands on experience in script development using scripting languages (e.g., Perl, Python, Bash, etc.) and Microservice and APIs (WireMock, postman, swagger, SQL, Jenkins, Integration to CICD pipeline). - Experience in a QA/test environment with a focus on technical, automated testing in a variety of environments (cloud, distributed or mainframe, business workflows and services/APIs, databases). - Very good communication skills, ability to focus, prioritize and solve complex technical problems. - Experience in DevOps with test integration, processes, and tools. - Experience in a variety of test automation tools (Robot Framework, Appium, Cucumber, Selenium, TestNG) and script development using scripting languages (e.g., Perl, Python, Gherkin, Bash, etc.). - Experience using source configuration tools (GIT, etc.). - Hands-on programming experience (e.g., Java, C, SQL DB querying). - Hands-on experience with DevOps Tools. Benefits - Leaders who support your development through coaching and managing opportunities. - The advantage of working with a dynamic, collaborative, and high performing team where initiative and hard work are recognized and rewarded. - Opportunity to do challenging work.

Role Description You will work collaboratively to improve our models and iterate on novel research directions, sometimes in just days. We're looking for talented engineers who'd enjoy applying their skills to deeply complex and novel AI problems. Here, you will: - Apply and extend the Helm proprietary algorithmic toolkit for unsupervised learning and perception problems at scale. - Carefully execute development and maintenance of tools used for deep learning experiments designed to provide new functionality for customers or address relevant corner cases in the system as a whole. - Work closely with software and autonomous vehicle engineers to deploy algorithms on internal and customer vehicle platforms. Qualifications - A sense of practical optimism: not all experiments are successful, but the ones that are more than make up for it! - Comfort operating in a fast-paced environment to deliver customer projects. - Introspection, thoughtfulness, and detail-orientation. - Experience working with neural networks, Tensorflow and/or PyTorch. - Fluency in Python and working knowledge of C/C++ programming. - A strong interest in unsupervised learning, computer vision, and/or the autonomous vehicle industry. - Master’s or Ph.D. in a related field and/or 5+ years of experience in a related field. Requirements The pay range for this position is estimated to fall in the base range of approximately $150,000 and $250,000. Base compensation for this position will vary based on location, qualifications, and relevant experience. The offered base salary may be above or below this range and compensation for the position may include additional compensation in the form of equity or a bonus/commission. Benefits - Competitive health insurance options. - 401K plan management. - Free lunch and fully-stocked kitchen in our South Bay office. - Additional perks: monthly wellness stipend, office set up allowance, company retreats, and more to come as we scale. - The opportunity to work on one of the most interesting, impactful problems of the decade. Company Description Helm.ai is proud to be an equal opportunity employer building a diverse and inclusive workforce. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. Any unsolicited resumes/candidate profiles submitted through our website or to personal email accounts of employees of Helm.ai are considered the property of Helm.ai and are not subject to payment of agency fees.

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.