Senior Machine Learning Engineer, Data Mining

Jobtailor; Massachusetts; On-site; USD 140,000 - 190,000; Full time

Job summary

Jobtailor is seeking an experienced ML Engineer to architect and train distilled models for multimodal data, and to develop RL-based data discovery and autonomous reasoning systems. You will optimize real-time deployment across GPU/CPU clusters, implement production-ready pipelines, and collaborate with ML scientists and data engineers.

The role emphasizes mentoring and shipping robust production-grade systems, with a focus on reliability, scalability, and operational excellence in a modern

Qualifications

• BS in Computer Science, Machine Learning, or related field, or equivalent professional experience.
• 6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment.
• Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models.
• Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning.
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
• Strong software engineering fundamentals: testing, CI/CD, containerization, and system design.
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference.
• Demonstrated ability to ship production-grade ML systems and mentor team members.
• Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers

Responsibilities

• Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data.
• Reinforcement Learning for Data Discover: Build RL-based policy learning and reasoning systems for autonomous driving applications.
• Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production.
• Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning and goal-directed behavior.
• Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization.
• Mentor and Collaborate: Translate research advances into scalable engineering solutions and guide junior engineers in best practices.

Skills

Model Distillation

Reinforcement Learning

Python Programming

ML Frameworks

Education

BS in Computer Science or related field

Tools

PyTorch

TensorFlow

JAX

AWS

GCP

Azure

Job description

Responsibilities

• Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data. Develop training pipelines for knowledge distillation. Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint.
• Reinforcement Learning for Data Discover: Build RL-based policy learning and reasoning systems for autonomous driving applications. Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. Explore reward shaping, environment modeling, and multi-agent RL where applicable.
• Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. Implement model versioning, A/B testing, and monitoring for performance regressions.
• Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. Integrate such systems into our broader autonomy stack as experimental or production components.
• Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence.
• Mentor and Collaborate: Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. Guide junior engineers in best practices for model training, evaluation, and deployment.

Requirements

• BS in Computer Science, Machine Learning, or related field, or equivalent professional experience.
• 6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment.
• Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models.
• Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning.
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
• Strong software engineering fundamentals: testing, CI/CD, containerization, and system design.
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference.
• Demonstrated ability to ship production-grade ML systems and mentor team members.
• Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers

Core Competencies

Expertise in designing and implementing machine learning models, particularly in model distillation and reinforcement learning, with a strong focus on optimizing deployment for real-time inference. Proven ability to mentor and collaborate with cross-functional teams to deliver production-grade systems.

Highest-signal resume keywords

• Model Distillation
• Reinforcement Learning
• Python Programming
• ML Frameworks (PyTorch, TensorFlow, JAX)
• Cloud Deployment (AWS, GCP, Azure)

ATS Optimization Keywords

Hard Skills

• Model Post Training
• Model Optimization
• Model Deployment
• Policy Optimization
• Reward Design
• Simulation Environments
• Testing
• CI/CD
• Containerization
• System Design

Soft Skills

• Mentoring
• Collaboration

Industry Keywords

• Machine Learning Engineering
• Autonomous Driving
• Agentic Systems
• Operational Excellence
• Fault Tolerance

Tools & Technologies

• GPU/CPU Clusters
• A/B Testing
• Monitoring Tools
• Omnitag