Role Description We are looking for a senior-level ML expert with deep experience in Speech AI, ideally focused on TTS / Voice Generation, to help build and scale production-grade speech systems. This is a highly hands-on role for someone who combines strong research capabilities with real-world production experience and can operate effectively in a fast-moving startup environment. Qualifications - 5+ years of experience in Speech AI, preferably TTS / Voice Generation - TTS has been a primary focus in recent years - Hands-on experience training and fine-tuning TTS models - Proven experience deploying ML models into real production environments - Strong understanding of inference, latency, scaling, monitoring, and reliability - Strong ML background overall (ML Scientist / ML Engineer trajectory) - Strong coding and engineering skills Requirements - Develop and improve TTS / Voice Generation models - Train, fine-tune, and evaluate speech models - Bring research ideas into production systems - Work across research, engineering, and product - Help define technical direction and ML best practices - Drive execution in a fast-changing environment - Collaborate closely with engineering, product, and business stakeholders Benefits - Experienced team, Acclaim is formed by a team of enthusiastic professionals who have created award-winning devices, voice assistants, and other AI-driven products for BigTech corporations - Cutting-edge technologies, we build technologies using our areas of expertise, including Computer Vision, Speech Technologies, Natural Language Understanding, Generative AI, etc. LLM and Diffusion models - Rapid career progression, facilitated by our team of seasoned senior professionals who hail from prestigious, industry-leading companies - Remote work opportunities from Europe / US - The company has prominent clients with an opportunity for you to work on different projects and/or to be involved in developing our proprietary products - A company with entrepreneurial spirit. We offer a secure workspace, thanks to the big clients we've raised, along with a true start-up culture.

• Own and scale ML-driven Marketing/Growth/Product capabilities • Lead development and deployment of core models, including Propensity, Churn prevention, Customer Lifetime Value models • Improve onboarding, targeting, personalization, and segmentation at scale • Continuously evaluate and improve marketing spend efficiency through ML-driven insights and models • Partner closely with Marketing, Product, and Finance to align ML investments with business priorities • Set standards for model development, experimentation, and validation • Design and optimize reward and incentive strategies to maximize user acquisition, activation, and retention

• Design and run experiments to improve agent quality: better tool use, better reasoning, better outputs, using frameworks like DSPy and VLLM • Build and maintain evaluation infrastructure to measure what's working and catch regressions before customers do • Optimize LLM inference: latency, cost, model routing, and quality tradeoffs • Partner with product teams on model selection and performance benchmarking • Work closely with product engineers and PMs to translate customer quality problems into ML hypotheses and solutions • Own models end-to-end: from research and experimentation to production deployment

• Conduct EDA and statistical profiling to identify trends and insights from data. • Perform feature engineering specifically for time-series forecasting. • Extract and transform data from relational databases (RDS, Oracle, PostgreSQL) into analytics-ready formats. • Develop pipelines for data ingestion and processing. • Build classical ML models for time-series forecasting, regression, and capacity/throughput modeling. • Evaluate model performance using metrics such as RMSE, MAE, and MAPE, documenting performance results. • Create insightful data visualizations and dashboards using Amazon QuickSight or equivalent BI tools. • Utilize the Python data stack (pandas, NumPy, scikit-learn, matplotlib/seaborn) for data manipulation and analysis. • Apply SHAP or other model explainability techniques to interpret model outputs. • Work closely with stakeholders to translate business rules into effective feature engineering pipelines. • Engage in milestone-driven, Firm Fixed Price delivery models, ensuring timely project completion.