Jobs at BJAK

Lead and execute high-priority cross-functional initiatives across engineering, product, hiring, operations, and business development. Build teams, processes, and capabilities; research markets and opportunities; identify operational problems and implement solutions; own projects end-to-end and deliver measurable business outcomes.

Design polished mobile-first and web interfaces for BJAK's AI and financial products. Create wireframes, user flows, prototypes and high-fidelity UI; maintain and evolve the design system; collaborate with product managers and engineers to deliver pixel-perfect implementations; iterate using user feedback, analytics and usability testing while ensuring accessibility and responsiveness.

Lead high-priority, cross-functional initiatives across engineering, product, hiring, operations, and business development. Build teams, processes, and capabilities; plan and execute projects end-to-end; research markets and products; identify operational improvements; and deliver measurable business outcomes through strong ownership and stakeholder management.

Lead architecture and engineering for AI-powered applications across backend, mobile, and desktop. Own reliability, observability, APIs, and model integrations. Grow and mentor a small senior applications team, drive production readiness, and make high-impact tradeoffs across latency, cost, security, and user experience to deliver reliable, low-latency AI-enabled workflows.

Lead product definition and execution for AI-powered assistant systems. Define end-to-end requirements, translate model capabilities into product decisions, design evaluations, balance trade-offs (quality, latency, cost, reliability), and work closely with ML, backend, and mobile engineers to ship reliable, user-trusted AI features.

Lead research and intelligence direction for a high-frequency AI assistant: set model and systems strategy (memory, reasoning, planning), choose architectures, define real-world evaluation and safety frameworks, drive frontier techniques, and partner with product and engineering to ship robust, production ML systems.

Design, build, and operate large-scale ML systems across data, training, evaluation, inference, and deployment. Lead GPU-optimized training and inference pipelines, ensure reliability, performance, safety, and seamless integration into products. Own production deployments, monitoring, and cross-functional best practices to drive measurable model improvements and efficient research-to-production cycles.

Lead the end-to-end ML execution: build and operate data pipelines, training and evaluation systems, and scalable inference. Fine-tune and optimize large models (LoRA/QLoRA/SFT/DPO/distillation), ensure GPU efficiency, monitor production quality, collaborate with product and application engineering, and rapidly ship reliable, safe ML solutions that translate research into production.

Lead the AI product strategy, collaborating with engineers to balance user needs and technical feasibility, ensuring quality and timely product delivery.

The role involves leading engineering teams and strategies for AI-powered applications, ensuring integration of ML capabilities into user-friendly systems across various platforms while promoting a culture of technical excellence.

The VP of Research, Machine Learning will set research direction for AI systems, focusing on context representation, reasoning, and evaluation frameworks while ensuring product safety and alignment.

Build and maintain cross-platform Electron desktop apps focused on reliability, performance, and real-time behavior. Implement desktop features (file access, notifications, auto-updates), integrate AI via backend APIs, optimize startup/memory, profile Electron overhead, manage large local state and message history, and ensure stability on macOS and Windows.

Lead production-grade ML systems: build data pipelines, training and evaluation workflows, and scalable GPU inference. Fine-tune and adapt large models (LoRA, QLoRA, SFT, DPO, distillation), optimize deployments for latency/cost/reliability, and collaborate with product and engineering to ship robust, observable model-driven features.

Lead the development of production-grade machine learning systems, build ML pipelines, and optimize models for deployment and performance.

Responsible for building and maintaining end-to-end ML pipelines and deploying production-grade ML systems. Tasks include model fine-tuning, evaluation, and collaboration with engineering teams.

You will build and own end-to-end ML systems, focusing on training, evaluation, deployment, and integration with applications, while ensuring quality and efficiency.

The role involves developing and maintaining cross-platform desktop applications using Electron, focusing on performance, reliability, and real-time collaboration features.

Work directly with leadership to drive high-priority cross-functional initiatives across engineering, product, hiring, operations, and business development. Lead projects end-to-end, build teams and processes, research markets and opportunities, and implement operational improvements to deliver measurable business outcomes.

Design, build, and operate ML infrastructure for training, evaluation, deployment, and inference. Improve reliability, scalability, latency, throughput, and cost. Create pipelines, observability, benchmarking, and tooling to enable fast experimentation and productionization of models while diagnosing regressions and bottlenecks.

Design, build, and ship LLM-powered applications and agent workflows. Integrate models with tools, databases, and services; develop prompting, tool-calling, retrieval, and evaluation systems; improve model behavior, reliability, latency, and cost; and establish observability, experimentation, and production practices for continuous improvement.