Role Description We're looking for an Applied Machine Learning Researcher to make our voice AI agents more natural, more reliable, and faster. You'll work on the core ML systems behind Phonely, fine-tuning models to beat frontier models on the conversational tasks that actually matter to our customers. This is applied research. Your work ships. Success here is measured in production model behavior getting better, not in prototypes or papers. You'll partner closely with engineering, product, QA, and the customer-facing teams to understand where models break in the real world, design rigorous experiments, improve the training data, and ship better models. NOTE: This is a hands-on research and engineering role. You'll write a lot of Python, dig through a lot of real production conversations, and own the numbers. It's demanding and it moves fast. This is a remote role, preferably based in Australia. What You'll Work On - Research and improve LLM behavior for real-time voice conversations - Design and run fine-tuning experiments across data, model, and evaluation strategies - Build evaluation frameworks for model quality, workflow-following, naturalness, reliability, task completion, and overall conversation quality - Analyze production conversations to find failure modes and opportunities to improve - Develop data curation, labeling, and synthetic data strategies - Compare model architectures, training approaches, prompts, and datasets - Investigate regressions and explain clearly why model behavior improves or degrades - Work with engineering to deploy research improvements safely and efficiently - Help define model release criteria, eval gates, and quality benchmarks Qualifications - Strong experience with machine learning and modern language models - Hands-on experience fine-tuning, evaluating, or adapting language models - Strong Python skills and comfort working with messy, real-world datasets - Ability to design rigorous experiments and interpret the results clearly - Experience building or improving evaluation systems for AI models - Strong analytical skills and the ability to debug model behavior - Clear written and spoken communication, so you can explain findings to technical and non-technical teammates alike - A practical mindset: you care about production impact, latency, reliability, and customer outcomes Nice to Have - A PhD in machine learning, NLP, or a related field - Experience with conversational AI, voice AI, or customer-support automation - Experience with SFT, preference tuning, DPO, RFT, GRPO, RLHF, LoRA, or QLoRA - Familiarity with model serving, inference optimization, or vLLM - Experience with synthetic data generation and data quality pipelines - Experience working in a startup or fast-moving product environment Why Join Phonely We're a group of ex-athletes, founders, and builders with low egos and a high belief that life is not about taking the easy road, but challenging ourselves to find the most we can be. Even with a remote team, we stay close: we're big on staying active, we back each other, and we care about the people we work with as much as the work itself. A few times a year we all get together in person and rent out Airbnbs in cool places (Rocky Mountains, Costa Rica, Indonesia) so you can see the world while on the grind. Interview Process - 15-minute intro call to evaluate fit - Deep dive on your ML and fine-tuning experience with a team member - Technical exercise or case study on a real model-quality problem - Final conversation with leadership

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) 505-3899. Learn more about Bright Vision Technologies at www.bvteck.com .

• Deliver robust, scalable ML solutions for AI capabilities in educational products, taking significant implementation decisions independently. • Work closely with Business Unit partners to understand their AI-related problems, help frame them clearly, and develop solutions that fit their actual needs. • Identify opportunities to reuse existing AI components and capabilities across teams. This includes use case discovery, technical solutioning, and making a credible case to partner teams for why reuse is the right path rather than building from scratch. • Review and validate the outputs of more junior scientists, providing expert-level technical feedback and catching issues before they reach production. • Shape the direction of projects within the team, bringing experience and a point of view to decisions about approach, tooling, and scope. • Propose new techniques and methods where current approaches have clear limitations, and take responsibility for evaluating and introducing them. • Mentor peers and support the growth of colleagues at earlier career stages, through code reviews, pairing, and direct feedback. • Maintain thorough documentation and uphold the technical health of deliverables over time, not just at point of delivery.

Role Description In this 3-month Internship, you will contribute your previous deal experience which will be used to help improve AI reasoning across investment banking workflows. The work involves generating, refining, and evaluating content that reflects how IB professionals think through transactions from origination and pitching through execution and close. - Draft realistic IB scenarios, prompts, and worked examples (pitch decks, comps, LBO models, CIMs, deal memos) that reflect real transaction workflows. - Evaluate AI-generated outputs against how a skilled analyst or associate would actually reason, flagging gaps in logic, technical rigour, or judgment. - Refine and rewrite model responses to improve their financial reasoning and structuring. - Partner closely with experts to translate domain expertise into training signal, giving feedback on what "good" looks like at each stage of a deal. - Identify edge cases and failure modes specific to IB workflows (e.g., sector-specific modelling conventions, regulatory nuance, negotiation dynamics). - Help document and systematise IB best practices into reusable frameworks model can learn from. Qualifications - Current student or recent graduate with a minimum of one IB internship or full-time role at a bulge bracket or elite boutique (e.g., Goldman Sachs, Morgan Stanley, J.P. Morgan, Bank of America, Citi, Barclays, UBS, Deutsche Bank, Wells Fargo, Evercore, Moelis, Centerview, Lazard, Jefferies). - Solid grounding in financial modelling, valuation methodologies (DCF, comps, precedent transactions), and deal mechanics. - High attention to detail and intellectual honesty to say "this output is wrong" even when it looks polished. - Sharp communication skills, able to explain why a piece of reasoning is right or wrong. - Comfortable with ambiguity and fast iteration. - Genuine curiosity about AI, even without a technical background. Benefits - Competitive salaries with an opportunity to join us full-time. - Modern, centrally located London office built for collaboration and focus. - Access to a range of AI tools to boost productivity and maximise results. - Tech, screens, and any equipment you need to perform at a high level. Logistics - Location: In-person (remote options available). - Start date: Immediate. - Referral bonus: For any successful referral hired into this role.