Staff Machine Learning Engineer

Jobgether; United States; Remote; USD 160,000 - 200,000; Full time

Benefits offered by this job

Competitive compensation

Annual bonus

Healthcare benefits

Retirement plan

Paid time off

Remote-first Canada

Job summary

Jobgether seeks an accomplished AI/ML leader to advance LLM research, design production-grade inference, and build scalable cloud-based systems. You will own end-to-end initiatives, collaborate across teams, and push for high-performance, cost-efficient solutions.

The role requires 8+ years of applied AI/ML experience, strong Python skills, and hands-on MLOps/LLMOps expertise. Remote-first work from eligible Canadian locations is supported, with occasional travel as needed.

Qualifications

• 8+ years of applied AI/ML experience with ownership of end-to-end systems.
• Strong Python proficiency and production-grade ML development experience.
• Solid foundations in statistical ML, transformers, and LLMs; understanding capabilities and limits.
• Extensive MLOps/LLMOps experience with non-deterministic AI evaluation at scale.
• Experience building cloud-based services on AWS, GCP, or Azure with real-time inference.
• Ability to work in ambiguous environments and drive solutions to production.

Responsibilities

• Advance LLM research and implementation: Research, design, and implement state-of-the-art large language model inference and orchestration techniques, translating emerging research into practical, scalable production solutions.
• Optimize model performance and efficiency: Identify opportunities to leverage smaller language models through fine-tuning, distillation, and other optimization techniques to improve performance, reduce operational costs, and meet application requirements.
• Build scalable AI/ML infrastructure: Design, develop, and operate robust machine learning systems and cloud-based services capable of supporting high-volume data processing, streaming workloads, and real-time inference at enterprise scale.
• Establish AI evaluation standards: Develop evaluation frameworks, metrics, and tooling for assessing a wide range of AI/ML capabilities, ensuring that model behavior, output quality, reliability, and performance can be measured consistently.
• Strengthen MLOps and LLMOps practices: Drive the design and implementation of operational foundations for deploying, monitoring, evaluating, and continuously improving machine learning models and LLM-powered applications.
• Develop and evaluate autonomous agents: Contribute to systems that support agentic workflows, including multi-turn interactions, contextual consistency, and reliable behavior across complex tasks.
• Drive technical innovation: Identify and integrate emerging AI development frameworks, tools, and techniques to accelerate engineering workflows and focus development efforts on high-value technical challenges.
• Own complex technical initiatives: Take end-to-end responsibility for ambitious projects, from initial exploration and experimentation through implementation, production deployment, and ongoing optimization.
• Collaborate across teams: Work effectively with engineering colleagues and other stakeholders to align AI/ML platform capabilities with product requirements and broader organizational objectives.
• Share technical expertise: Contribute to engineering excellence through technical documentation, knowledge sharing, research, open-source contributions, and other activities that advance the wider machine learning community.

Skills

Python

ML engineering

MLOps/LLMOps

Cloud platforms

Research leadership

Communication

Adaptability

Fast learning

Tools

Claude

Codex

GitHub Copilot

Cursor

Job description

Accountabilities

• Advance LLM research and implementation: Research, design, and implement state-of-the-art large language model inference and orchestration techniques, translating emerging research into practical, scalable production solutions.
• Optimize model performance and efficiency: Identify opportunities to leverage smaller language models through fine-tuning, distillation, and other optimization techniques to improve performance, reduce operational costs, and meet application requirements.
• Build scalable AI/ML infrastructure: Design, develop, and operate robust machine learning systems and cloud-based services capable of supporting high-volume data processing, streaming workloads, and real-time inference at enterprise scale.
• Establish AI evaluation standards: Develop evaluation frameworks, metrics, and tooling for assessing a wide range of AI/ML capabilities, ensuring that model behavior, output quality, reliability, and performance can be measured consistently.
• Strengthen MLOps and LLMOps practices: Drive the design and implementation of operational foundations for deploying, monitoring, evaluating, and continuously improving machine learning models and LLM-powered applications.
• Develop and evaluate autonomous agents: Contribute to systems that support agentic workflows, including multi-turn interactions, contextual consistency, and reliable behavior across complex tasks.
• Drive technical innovation: Identify and integrate emerging AI development frameworks, tools, and techniques to accelerate engineering workflows and focus development efforts on high-value technical challenges.
• Own complex technical initiatives: Take end-to-end responsibility for ambitious projects, from initial exploration and experimentation through implementation, production deployment, and ongoing optimization.
• Collaborate across teams: Work effectively with engineering colleagues and other stakeholders to align AI/ML platform capabilities with product requirements and broader organizational objectives.
• Share technical expertise: Contribute to engineering excellence through technical documentation, knowledge sharing, research, open-source contributions, and other activities that advance the wider machine learning community.

Requirements

• At least 8 years of applied AI/ML experience, with a demonstrated track record of owning technically ambitious systems end to end and making meaningful architectural and implementation decisions.
• Strong proficiency in Python and experience developing production‑grade machine learning applications and services.
• Solid foundations in statistical machine learning, transformer architectures, and large language models, including an understanding of their capabilities, limitations, and evaluation requirements.
• Extensive experience with MLOps and LLMOps patterns, including designing and implementing rigorous evaluation metrics for non‑deterministic AI features at scale.
• Proven experience building cloud‑based services on AWS, GCP, Azure, or comparable platforms, particularly systems involving high‑volume data, streaming pipelines, real‑time inference, and diverse data stores.
• Demonstrated ability to operate in early‑stage or ambiguous technical environments, define solutions from limited initial requirements, and move projects from concept to production with a strong bias for action.
• Strong problem‑solving and analytical skills, with the ability to investigate complex technical challenges and make sound engineering decisions.
• Excellent communication and collaboration skills, including the ability to explain complex concepts, influence technical direction, and work effectively with teammates across functions.
• Familiarity with AI‑assisted development tools such as Claude, Codex, GitHub Copilot, Cursor, or comparable technologies.
• Ability to rapidly learn new technical frameworks, adapt to evolving AI capabilities, and maintain high engineering standards in a fast‑paced environment.

Additional qualifications considered an asset:

• Experience building evaluation frameworks for autonomous agents and large language models.
• Experience developing autonomous agents that support multi‑turn conversations and maintain long‑term context and consistency.
• Previous experience working with globally distributed engineering teams.
• Evidence of technical leadership and impact through open‑source machine learning or data projects, published research papers, technical presentations, or engineering blog posts.

Benefits

• Competitive compensation: Estimated annual salary range of $160,320–$200,400, with a target annual bonus of 12.5%. Final compensation depends on relevant skills, experience, and geographic location.
• Additional compensation opportunities: Depending on role eligibility, access to incentive programs, equity grants, and other compensation components.
• Comprehensive healthcare: Health and wellness benefits designed to support employees' physical and mental well‑being, with specific offerings varying by location.
• Retirement savings: Access to a retirement savings program, with applicable contributions and plan details determined by location and eligibility.
• Generous paid time off: Vacation, personal time, and other paid leave options, with specific entitlements varying by location.
• Parental and wellness leave: Dedicated leave programs that support family responsibilities, personal well‑being, and work‑life balance.
• Remote‑first work environment: Work remotely from eligible locations in Ontario, British Columbia, or Alberta, Canada, with occasional travel for team gatherings, project meetings, or customer engagements as required.
• Professional growth: Opportunities to work with advanced AI/ML technologies, explore emerging research, and develop expertise in production‑scale intelligent systems.
• Collaborative global culture: Join a distributed team that values inclusion, knowledge sharing, innovation, and meaningful technical contributions.
• Community engagement: Opportunities to support volunteering, charitable donations, and positive change within local communities.