Applied AI Engineer
WFA Digital Insight
The demand for skilled AI engineers has skyrocketed in recent years, with a notable 25% increase in job openings in the last year alone. As the industry shifts towards more practical applications of AI, professionals with expertise in machine learning, systems, and product development are in high demand. Bjak, a pioneer in proactive AI smart assistants, is seeking an Applied AI Engineer to turn model capabilities into real product behavior. With the global AI market projected to reach
Job Description
About the Role
The Applied AI Engineer role at Bjak is a unique opportunity to work at the intersection of machine learning, systems, and product development. As an Applied AI Engineer, you will be responsible for turning model capabilities into real product behavior, ensuring that AI-powered features meet the required accuracy, latency, and reliability targets. This role is critical to Bjak's mission of bringing intelligence to everyday conversations, errands, organizing, and workflows.The Applied AI Engineer will work closely with the product and engineering teams to translate ambiguous problems into working systems. This will involve designing and iterating on prompts, tools, memory, and agent workflows, as well as debugging issues across the full stack. The ideal candidate will have a strong foundation in machine learning and modern neural network architectures, with hands-on experience in training, fine-tuning, or deploying ML models.
Bjak's team is committed to delivering high-quality work while maintaining a rapid pace. The company values transparency, efficiency, and collective decision-making, making it an ideal environment for professionals who thrive in fast-moving environments.
What You Will Do
- Design and iterate on prompts, tools, memory, and agent workflows to achieve high reliability for long-running workflows
- Build and ship AI features end-to-end, from model development to production deployment
- Turn raw model outputs into structured, reliable, and predictable behaviors
- Debug issues across the full stack, including model, orchestration, infrastructure, and UX
- Optimize for latency, cost, and production reliability
- Develop lightweight evaluation frameworks to measure real-world performance
- Collaborate with product and engineering teams to translate ambiguous problems into working systems
- Develop and maintain data pipelines, training loops, and inference systems
- Identify and address production issues quickly and effectively
- Iterate on models and systems based on real-world signals and measurable improvements
What We Are Looking For
- Strong foundation in machine learning and modern neural network architectures
- Hands-on experience with training, fine-tuning, or deploying ML models
- Ability to write clean, production-quality code
- Comfort working across abstraction layers (model, infrastructure, product)
- Strong problem-solving skills in ambiguous, fast-moving environments
- Experience with Python, PyTorch, or JAX
- Knowledge of LLMs, inference, and serving technologies
- Experience with vector databases and data pipelines
- Strong communication and collaboration skills
- Ability to work independently and bring structure to complex problems
Nice to Have
- Experience with cloud-based infrastructure and containerization
- Knowledge of agile development methodologies and version control systems
- Familiarity with DevOps tools and practices
- Experience with machine learning model interpretability and explainability
- Certification in machine learning or related fields
Benefits and Perks
- Competitive salary and benefits package
- Opportunity to work on cutting-edge AI technology
- Collaborative and dynamic work environment
- Flexible working hours and remote work options
- Access to professional development and training opportunities
- Health and wellness programs
- Retirement savings plan
- Paid time off and holidays
- Employee discounts and perks
How to Stand Out
- Tailor your resume: Highlight your experience with machine learning, systems, and product development, and be sure to include relevant tools and technologies.
- Prepare for technical interviews: Review common machine learning and systems interview questions, and practice whiteboarding exercises to improve your problem-solving skills.
- Develop a personal project: Showcase your skills by building a personal project that demonstrates your expertise in machine learning and AI.
- Research the company: Learn about Bjak's mission, values, and products, and be prepared to discuss how your skills and experience align with their goals.
- Practice your communication skills: Be prepared to explain complex technical concepts in simple terms, and practice your collaboration and teamwork skills.
- Be prepared to discuss your experience with remote work: Highlight your ability to work independently and effectively in a remote environment.
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