Senior Machine Learning Engineer

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Senior Machine Learning Engineer Needed

Remote-San Diego/$150k-$200k/Military Defense & Civil Security

This Jobot Job is hosted by: Michael Oktay

Salary: $150,000 - $200,000 per year

A bit about us:

Based in Oceanside, CA we serve the global market with products, services, and solutions from military defense to civil security.

Why join us?

• Remote
• Competitive Compensation
• Comprehensive Medical, Dental, Vision Insurance
• 401(k) w/ Match
• PTO/Sick/Holiday/Vacation Pay

Job Details

Responsibilities:

• Be the resident expert and continuously monitor the SOTA in machine learning (ML), deep learning (DL), reinforcement learning (RL) and multi-modal perception methods as enablers for a variety of Autonomous system applications. Stay informed and be up-to-speed on all relevant trends, advancements and breakthroughs.
• Ability to operate as an engineering point of contact for senior leadership and customers.
• Create and shape an inclusive, collaborative, and inspiring working culture in working closely with teams of professors and graduate students in the co-development of solutions to satisfy all customer expectations and milestones.
• Assist university project PI's in managing and progressing all project milestones by continuously monitoring progress, pre-empting roadblocks and providing actionable solutions.
• Lead the technology transfer of research results to company internal applied engineering teams and assist in productionizing in a co-development mode.
• In collaboration with the various engineering functions within the company, identify applications and uses case that can benefit from ML/RL/Perception. Work closely with product SMEs to develop solutions for enhancing product capabilities.
• Create and publish high quality, applied and research, publications and represent the company in US defense forums and at top conferences such as, RSS, ICRA, CoRL, CVPR, ICLR, ICML, NeurIPS, ICCV, AAAI.
• Support all internal strategic planning and operations reviews including post hoc analyses and lessons learnt. Support PI's with milestone reviews with internal and external customers.
• Mentor junior engineers and assist with learning and knowledge share related to ML/DL/RL within the company.
• Ensure effective and efficient implementation of planning and operation processes, assisting the program manager in understanding technical progress and forecasting future work requirements.
• Socialize key milestones with industry, academia, and government through conference presentations, technical paper publications, and media relations.

Required Skills/Experience:

• Advanced degree in Engineering, Machine Learning, Robotics, or a related analytical discipline.
• R&D experience, MS (7+yrs), PhD (3+ yrs), in Machine Learning, RL and multi-modal Perception with demonstrated applications in one or more areas of Robotics, Autonomous vehicles, UxV's or a related area.
• Demonstrable in depth understanding of perception guided Reinforcement Learning/Neural-Control for real world use cases.
• Deep expertise in RL algorithms, including model free methods (deep reinforcement learning, policy gradient methods, value-based methods, A2C etc.) and model-based approaches.
• Proficiency in programming languages such as Python, with experience in deep learning frameworks (e.g., TensorFlow, PyTorch, Keras) and RL libraries (e.g., OpenAI Gym, Stable Baselines, MuJoCo, PyBullet, etc.) and robotics platforms (e.g., ROS).
• Strong analytical and problem-solving skills, with the ability to translate theoretical concepts into practical solutions and prototype implementations.
• Experience with mission analysis, customization, and development of product, managing project risk, deployment of product, user training program management, customer support, and product sustainment and maintenance.
• Willingness to interface on an interdepartmental level and be an agent of change for product modernization via practical and strategic adoption of emergent AI/ML technologies.
• Strong interpersonal skills and an ability to build effective working relationships is a must, especially across government and industry.
• Excellent oral and written communication skills with a proven ability to communicate well with peers and different levels of management and the customer.
• Proven leader who motivates, inspires, and teaches others.
• Strong work ethic and self-motivate and pro-active in seeking out the best solution/s.
• Must exhibit leadership for executing practical application of engineering processes on a development program and maintaining engineering rigor while balancing cost, technical, and schedule.
• Strong technical leadership with a research-to-product mindset, including experience leading teams of senior researchers and engineers.

Nice to Have:

• Demonstrated experience in developing and applying SOTA CV solutions in, object detection, collision avoidance, path planning, navigation and SLAM in applications such as AV's, UxV's, Robotic navigation.
• Demonstrated experience in working with multimodal perception modalities and good understanding of DL based CV architectures, including few-shot models such as YOLO, Faster R-CNN, SSD etc.
• Demonstrated research publications in major conferences (RSS, ICRA, CoRL, CVPR, ICLR, ICML, NeurIPS, ICCV, AAAI, etc.)
• Experience developing and deploying RL algorithms on embedded systems or edge devices for real-time applications.
• Knowledge of specific domains relevant to one or more autonomous systems such as UAVs, USVs, UUVs.
• Experience with multimodal sensor fusion and fusion techniques for perception applications is a strong plus. Experience with 3D data and representations (pointclouds, meshes, etc.)
• Experience with novel pipelines and architectures for convolutional neural nets and Vision Transformers.
• Experience leveraging backbones for novel purpose-built use cases such as, MTL, edge deployment backbones with Mobilnet, etc are a plus.
• Deep mathematical/statistical understanding of learning architectures, learning theory, uncertainty quantification, explainability, etc. is a plus.
• Experience with Multi-Agent Systems, Game Theory, Bayesian Networks.
• Experience in Modern control theory including Adaptive/Robust control, Optimal Control, MPC, Neural decision making, Barrier functions, Reachability etc.
• Experience with Safe-RL, Hierarchical RL, Massively Parallel RL, RLHF.
• Experience with simulation environments for training and testing autonomous systems, such as Gazebo, AirSim, or PX4 SITL.
• Demonstrated experience leading fundamental and/or applied research projects focused on autonomous systems.
• Experience working in the DoD contracting industry.

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