• Successfully develop and implement machine learning models that optimize financial processes. • Improving and building new ML models into production for new project lines. • Generate new insights to better understand clients using ML. • Research and fast insights to improve actual ML models implemented in Baubap. • Optimize and refine models to achieve desired accuracy and efficiency. • Understand, propose, design, implement, and bring ML solutions into production. • Create POCs researching viability for new features, data sources, and algorithm optimization. • Propose initiatives to improve Credit Risk Models. • Coordinate with Data Engineers for successful implementation of pipelines for new features. • Collaborate with engineering and product teams to integrate ML algorithms.

• Strategic Model Integration: Evaluation and integration of open-source and commercial models (e.g., for ASR, TTS, diarization, VAD), balancing quality, latency, and cost to accelerate game development. • Model R&D & Adaptation: Lead the fine-tuning and adaptation of speech models (TTS, Voice Cloning, ASR) using parameter-efficient techniques (e.g., LoRA/PEFT) to address production gaps and creative constraints that external/commercial solutions cannot satisfy. • Data & Alignment: Lead audio and multimodal data curation, including cleaning, segmentation, labeling, and synthetic data generation to support speech model training and adaptation. • Scalable Training: Scale speech model training using distributed data pipelines and training strategies for large datasets.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description As a Senior Machine Learning Engineer at Striveworks, you’ll be challenged—and trusted—on day one to be a core contributor to both the customer-driven projects and the enduring products of the company. You will be a key Striveworks solutions provider to project sites and customers where Chariot, our proprietary data platform, is deployed. You will inform, envision, and help extend Striveworks’ core software products. You will work alongside data scientists, software engineers, and DevOps engineers to transform machine learning models into operational capabilities. Your day-to-day will include: - Working with customers, engineers, and other stakeholders to define clear requirements that solve customers’ problems and leverage the capabilities of our AI operations platform - Developing machine learning models and custom analytics that are applied to image, video, text, geospatial, time series, and structured data - Orchestrating and automating complex data engineering and analytic pipelines - Envisioning, specifying, and, at times, designing and implementing core product functionality - Opportunities to conduct mission-critical field work This position offers a fully remote work environment, or you can work hybrid/on site at our office in northwest Austin, TX, with up to 25% travel. Qualifications - BS degree in computer science, machine learning, or a related discipline and 6+ years relevant experience - Demonstrated experience delivering data-centric systems (e.g., data engineering, data cleaning, ETL pipelines, machine learning, and other production analytics) - Proficiency in programming languages and libraries common to machine learning; excellence in Python is essential, as is knowledge of libraries like TensorFlow, PyTorch, and/or scikit-learn - Proficiency in software engineering fundamentals to include algorithms, data structures, design patterns, and at least one systems programming language (e.g., Go, Rust, C++, Java, Scala, etc.) - Proficiency with modern software engineering tools and processes (Agile, version control, issue tracking, CI/CD, debugging, etc.) - Eligibility and willingness to obtain and maintain a Secret (or above) US security clearance - Due to the nature of this role, candidates must have US citizenship Requirements - Advanced degree in data science, machine learning, computer science, or a related discipline - Knowledge of relevant architectures and design patterns for client-server systems (e.g., asynchronous programming, REST, GraphQL, React, Vue, Angular) - Experience implementing and deploying software into containerized or cloud environments (e.g., Docker, Kubernetes [k8s], infrastructure as code, major cloud architectures) - Experience with a variety of unstructured data types (e.g., imagery, full motion video, text, acoustic, sonar, RF, telemetry signals) - Experience building agentic systems, agentic workflows, or AI agents - Experience defining, scoping, planning, and delivering complex technical solutions - Experience leading a small team - Experience delivering technology solutions in secure government environments - Active Secret (or above) US security clearance Benefits - Medical/dental/vision insurance - Voluntary life, long-term disability, accident, and hospital indemnity insurance - HSA and FSA (including dependent care FSA) plans - 401(k) plan - Unlimited PTO - Paid parental leave

• Designing and building the ML/LLM solution for data ingestion, knowledge extraction, retrieval, and subsequent reasoning. • Creating the datasets, metrics, and pipelines that drive measurable improvements across the system. • Architecting and improving agents for context retrieval, knowledge extraction, and data alignment, which includes prompt engineering, model selection, and inference optimization. • Establishing MLOps practices, including orchestration, observability, and experiment tracking. • Collaborating with the engineering team on system design and with JetBrains Research on the research agenda. • Defining hiring criteria, growing the ML team, and shaping the ML team culture.