Principal Machine Learning Engineer
WFA Digital Insight
The demand for skilled machine learning engineers has skyrocketed, with a 25% increase in job postings over the past year. As companies like Bjak integrate AI into their products, the need for experts who can design and deploy large-scale ML systems has never been more pressing. With the rise of remote work, professionals with strong backgrounds in deep learning and transformer-based architectures are in high demand. Bjak stands out for its commitment to building a proactive AI smart assistant, and candidates should be prepared to showcase their technical expertise and ability to work independently.
Job Description
About the Role
As a Principal Machine Learning Engineer at Bjak, you will be responsible for designing and evolving the company's most critical ML systems. This is a hands-on, high-impact role that requires a deep technical authority and the ability to set the technical standard for how ML systems are built across the organization. You will operate across training, inference, evaluation, and infrastructure, solving the most challenging architectural and performance problems.The role entails working closely with cross-functional teams to integrate ML systems into backend, mobile, and desktop products. You will be responsible for making pragmatic trade-offs and shipping improvements quickly, learning from real usage and iterating on the design of ML systems. This is a unique opportunity to work on a product that has the potential to revolutionize the way people interact with technology.
Bjak's team is comprised of talented professionals who are passionate about building a proactive AI smart assistant. As a Principal Machine Learning Engineer, you will be an integral part of this team, providing technical guidance and expertise to ensure the successful development and deployment of ML systems.
What You Will Do
- Architect and build large-scale ML systems spanning data, training, evaluation, inference, and deployment
- Design reproducible, high-performance training pipelines across GPU infrastructure
- Architect inference systems that balance latency, throughput, cost, and reliability at scale
- Design and maintain data systems for high-quality synthetic and real-world training data
- Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership
- Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies
- Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products
- Work under real production constraints: latency, cost, reliability, and safety
- Make data-driven decisions and drive the development of ML systems based on insights and findings
What We Are Looking For
- Strong background in deep learning and transformer-based architectures
- Hands-on experience training, fine-tuning, or deploying large-scale ML models in production
- Proficiency with at least one modern ML framework (e.g. PyTorch, JAX), and ability to learn others quickly
- Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, ZeRO, Ray)
- Strong software engineering fundamentals – you write robust, maintainable, production-grade systems
- Experience with GPU optimization, including memory efficiency, quantization, and mixed precision
- Comfort owning ambiguous, zero-to-one ML systems end-to-end
- A bias toward shipping, learning fast, and improving systems through iteration
- Experience working in a remote environment and collaborating with distributed teams
Nice to Have
- Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer
- Contributions to open-source ML or systems libraries
- Background in scientific computing, compilers, or GPU kernels
- Experience with RLHF pipelines (PPO, DPO, ORPO)
- Experience training or deploying multimodal or diffusion models
Benefits and Perks
- Competitive compensation package
- Opportunity to work on a cutting-edge AI product
- Collaborative and dynamic work environment
- Flexible remote work arrangement
- Access to the latest tools and technologies
- Professional development opportunities
- Comprehensive health insurance
- Generous paid time off policy
How to Stand Out
- Develop a strong understanding of deep learning fundamentals, including transformer-based architectures and large-scale ML model deployment.
- Showcase your ability to work with distributed training and inference frameworks, and highlight your experience with GPU optimization.
- Create a portfolio that demonstrates your expertise in designing and deploying ML systems, and be prepared to discuss your approach to solving complex technical problems.
- Prepare to discuss your experience working in a remote environment, and highlight your ability to collaborate with distributed teams.
- Be ready to provide specific examples of your experience with ML frameworks, such as PyTorch or JAX, and highlight your ability to learn new technologies quickly.
- Research the company's product and technology stack, and be prepared to discuss how your skills and experience align with their goals and vision.
- Prepare to discuss your approach to data-driven decision making, and highlight your ability to drive the development of ML systems based on insights and findings.
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