• Design and build agentic workflows for chat experiences and asynchronous background processes • Choose and apply the right techniques for prompting, tool use, orchestration, memory, and evaluation • Write high-quality, production-grade Python • Iteratively build, test, deploy, and operate services on Azure • Work closely with a small, fast-moving, fully remote team

• Support product teams in building new AI-enabled features • Act as a bridge between product management, data engineering, and software engineering • Develop and maintain ML and AI infrastructure • Contribute to data engineering and ensure data quality • Contribute to design and execution of data science projects

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description As a founding Applied AI Engineer at Valence, you will help define and build the future of AI-powered leadership coaching, working directly with our Head of AI and cross-functional product and engineering teams. You’ll be at the intersection of generative AI research and product execution, helping design, build, and refine intelligent systems that deliver context-aware, personalized coaching at enterprise scale. This role is purposefully broad and adaptable: we don’t expect any one candidate to check every technical box, but we do look for engineers who are eager to learn, iterate rapidly, and contribute meaningfully across a range of challenges from model behavior to production systems. - Tackle real-world AI problems: - Transform enterprise data into actionable context - Optimize conversational experiences - Shape how AI engages with users in meaningful and responsible ways - Your work will directly impact how our platform performs in high-stakes enterprise deployments and how leaders around the world grow through AI-facilitated insights. Qualifications - 3+ years of experience in software engineering, AI/ML, data-intensive systems, AI/ML development (ideally including a Master's or Ph.D. in Computer Science, ML, Data Science, or a related field) - Familiarity with language systems (e.g., NLP, conversational interfaces, IR) and comfort reasoning about model behavior, context, and evaluation - both theoretical and practical knowledge - Experience with core data science tools such as NumPy, scikit-learn, Pandas, PySpark, plus SQL and common visualization tools (e.g., matplotlib, Seaborn, Plotly, or BI tools) - Comfortable developing and deploying services in cloud environments (AWS, GCP, Azure) and working with containerization/orchestration (Docker, Kubernetes) - Strong software engineering skills, including writing maintainable code, debugging distributed systems, and collaborating in cross-functional teams - Eagerness to tackle unfamiliar problems, learn new technologies, and contribute to shaping our platform and culture - Ability to explain technical ideas clearly and work effectively with both technical and non-technical stakeholders Requirements - Experience with ML lifecycle tools (e.g., MLflow, Weights & Biases) - Familiarity with Cloud ML services - Past work building generative AI applications Benefits - Ownership & Rapid Growth - Outsized missions from day one, with direct responsibility for company-defining projects - Work alongside the executive team with transparency into strategy and decision-making - Influence on direction through real-time customer feedback and market insights - AI-First Operator - Work directly with cutting-edge AI models and next-generation platforms - Build expertise in enterprise AI implementation across Fortune 500 companies and multiple industries - Establish yourself as a recognized leader among peers in shaping how AI transforms work at a global scale - Compensation - Competitive salary including base + bonuses - Comprehensive health coverage (medical, dental, vision) from day one - Generous PTO, company-wide R&R shutdowns, and paid parental leave - Retirement plan support for US and global employees - Equity - Meaningful ownership in a venture-backed company at a growth inflection point - Financial upside that comes from scaling fast - Top-up grants as we scale and you deliver exceptional performance — your compensation grows alongside your impact - Top-Performing Culture - A culture built for top talent: intensity to win, growth without limits, and a team that solves hard problems and celebrates big wins together Location and Work Environment - If candidates are based in NYC or Toronto they can work hybrid in our offices, otherwise this role can be remote. - Candidates must be comfortable working with colleagues in different time zones (UK), and have valid travel documents without work authorization restrictions in the US. Diversity and Inclusion - We are dedicated to creating a diverse and inclusive environment where everyone feels valued and supported. - We encourage applications from candidates of all backgrounds and offer accommodations upon request throughout the hiring process. Employment Verification & Commitment - We use third-party services to verify employment history, education, and other information relevant to your candidacy. - Employment is contingent upon the successful completion of these verification checks. - This is a full-time role that requires a high level of focus, availability, and commitment. - Employees may not hold concurrent full-time employment with another organization while employed at Valence. - Any outside consulting, advisory, freelance, or other professional work must be disclosed and approved in advance and must not interfere with job responsibilities, availability, performance, or create a conflict of interest.

• Research, implement, and validate model architecture and algorithm changes that improve video generation fidelity, with emphasis on human-centric quality (identity preservation, anatomy, motion coherence, and interaction realism) • Explore and prototype improvements across spatial multimodal modeling, modality alignment, flow-based or diffusion-based video generation, and neural rendering-inspired representations to improve controllability and long-horizon consistency • Improve training and inference efficiency through architectural and post-training techniques (compute/memory optimizations, distillation, pruning, and compression) • Define model training objectives that improve sim-to-real and real-to-sim generalization, especially for human motion, contact, and interaction dynamics across real-world and synthetic/simulation data • Develop detailed, domain-specific benchmarks for evaluating world foundation models, especially generation and understanding world models that reason about video, simulation, and physical environments • Translate research results into robust implementations like training code, production-grade checkpoints, model integrations, and demos that clearly showcase capability gains across teams