San Jose, CA, USA

We are looking for a hands-on, systems-oriented AI Agent Engineer to design, build, and maintain intelligent agents that drive automation and business impact across the enterprise. This role is responsible for the full lifecycle of agent development — from design to versioning, orchestration, and continuous learning.

You’ll contribute directly to scaling our AI strategy by engineering reusable components, optimizing agent workflows, and ensuring real-world performance in production environments.

What you'll Do

• Agent Development: Build and fine-tune specialized AI agents for targeted customer experience use cases such as discovery, support, and lead qualification. Implement prompt engineering strategies, memory handling, resource management and tool-calling integrations

• Multi-Agent Communication: Adopt agent-to-agent communication protocols and handoff mechanisms to enable cooperative task execution and delegation. Build orchestrated workflows across agents using frameworks like LangChain, AutoGen, or Semantic Kernel

• Templates & Reusability: Create reusable agent templates and modular components to accelerate deployment across business units. Build plug-and-play configurations for domain-specific requirements.

• Lifecycle Management & Monitoring: Track and improve conversation quality, task success rate, user satisfaction, and performance metrics. Automate monitoring of agent behavior using observability tools (e.g., Arize, LangSmith, custom dashboards)

• Continuous Improvement: Implement learning workflows, including human-in-the-loop feedback and automatic retraining. Refine prompts and model behavior through structured experimentation and feedback loops.

• Maintenance & Governance: Handle knowledge base updates, drift detection, performance degradation, and integration of new business logic. Ensure agents stay aligned with evolving enterprise data sources and compliance requirements

• Deployment: Manage agent versioning, testing pipelines (unit, regression, UX), and controlled rollout processes. Collaborate with DevOps, QA, and infrastructure teams to ensure scalable deployments

What you need to succeed - 3–5+ years of experience in AI/ML engineering, NLP systems, or backend development - Strong proficiency with LLM frameworks (e.g., OpenAI APIs, LangChain, RAG pipelines) - Experience building conversational agents or workflow bots in production environments - Familiarity with cloud platforms (AWS/GCP/Azure), REST APIs, Python, and containerization (Docker, K8s) - Comfort with prompt design, vector databases, and memory handling strategies

Preferred Qualifications - Experience with multi-agent frameworks or agent orchestration systems - Familiarity with observability tools, data labeling workflows, or synthetic data generation - Background in conversational design or dialogue management systems - Degree in Computer Science, Data Science, Engineering, or a related field

Bala Cynwyd (Philadelphia Area), Pennsylvania United States

Overview

We’re looking for a Machine Learning Systems Engineer to strengthen the performance and scalability of our distributed training infrastructure. In this role, you'll work closely with researchers to streamline the development and execution of large-scale training runs, helping them make the most of our compute resources. You’ll contribute to building tools that make distributed training more efficient and accessible, while continuously refining system performance through careful analysis and optimization. This position is a great fit for someone who enjoys working at the intersection of distributed systems and machine learning, values high-performance code, and has an interest in supporting innovative machine learning efforts.

What You’ll Do

Collaborate with researchers to enable them to develop systems-efficient models and architectures Apply the latest techniques to our internal training runs to achieve impressive hardware efficiency for our training runs Create tooling to help researchers distribute their training jobs more effectively Profile and optimize our training runs

What we're looking for Experience with large-scale ML training pipelines and distributed training frameworks Strong software engineering skills in python Passion for diving deep into systems implementations and understanding fundamentals to improve their performance and maintainability Experience improving resource efficiency across distributed computing environments by leveraging profiling, benchmarking, and implementing system-level optimizations

Why Join Us?

Susquehanna is a global quantitative trading firm that combines deep research, cutting-edge technology, and a collaborative culture. We build most of our systems from the ground up, and innovation is at the core of everything we do. As a Machine Learning Systems Engineer, you’ll play a critical role in shaping the future of AI at Susquehanna — enabling research at scale, accelerating experimentation, and helping unlock new opportunities across the firm.

Noumenal Labs | Remote-friendly | Full-time

Noumenal’s Probabilistic Perception Lab builds systems capable of navigating outdoor environments through probabilistic spatial reasoning and structured uncertainty reduction. We are looking for a Research Engineer with deep experience in probabilistic inference, spatial AI, and structured generative models to drive applied breakthroughs in perception of outdoor environments. You will work closely with researchers, systems engineers, commercial software engineers, and roboticists to build models that integrate 3D geometry, scene composition, uncertainty, and adaptive inference grounded in generative representations. This role is ideal for someone who has operated at the intersection of probabilistic computing, 3D scene understanding, computational neuroscience, and machine learning research, with experience spanning both foundational research and scalable, applied engineering.

What You’ll Do

~ Develop and deploy probabilistic generative models for perception, scene understanding, and spatial reasoning (structured generative models, inverse graphics, Bayesian scene reconstruction) on hardware in a commercial product. ~ Build inference engines for SLAM, 3D reconstruction, object-centric scene modeling, and spatial world models, leveraging MCMC, variational inference, or novel structured inference techniques. ~ Design systems that combine topological, geometric, and probabilistic methods for robust representation of spatial and conceptual structure. ~ Lead and engage in directed engineering efforts to translate novel algorithms into performant systems suited for real-time or near–real-time perception. ~ Collaborate with researchers in probabilistic computing, robotics, and AI to prototype, test, and iterate on models using synthetic and real sensory data.

Required Skills

~ Experience building perception systems in robotics. ~ Ability to translate research concepts into robust, scalable engineering implementations. ~ Strong coding ability in Python and modern ML frameworks (PyTorch, JAX, or TensorFlow). ~ Expertise in probabilistic inference, structured generative models, or Bayesian approaches (MCMC, variational inference, factorized models, hierarchical generative models). ~ Experience in 3D perception and spatial AI, including at least one of: SLAM, object-centric modeling, structured scene representations, or probabilistic inverse graphics frameworks. ~ Commitment to open-source contributions and internal cross-lab collaborations.

Ideal Background

~ Experience with topological data analysis, geometric representations, or mathematical structure in learning systems (e.g., planning in latent spaces). ~ Strong mathematical background (geometry, topology, optimization, or probabilistic modeling). ~ Background working in interdisciplinary research groups (AI, neuroscience, robotics, mathematics). ~ Publications in machine learning, probabilistic modeling, computational neuroscience, or mathematical methods for perception.

What We Offer

~ Close collaboration with researchers in robotics, physics-inspired AI, and spatial intelligence. ~ Access to real-world data for 3D perception and inference experiments. ~ A remote-friendly environment, flexible work culture, competitive salary + equity.

Bala Cynwyd (Philadelphia Area), Pennsylvania United States

Overview

Susquehanna is expanding the Machine Learning group and seeking exceptional researchers to join our dynamic team. As a Machine Learning Researcher, you will apply advanced ML techniques to a wide range of forecasting challenges, including time series analysis, natural language understanding, and more. Your work will directly influence our trading strategies and decision-making processes.

This is a unique opportunity to work at the intersection of cutting-edge research and real-world impact, leveraging one of the highest-quality financial datasets in the industry.

What You’ll Do

Conduct research and develop ML models to enhance trading strategies, with a focus on deep learning and scalable deployment Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches Design and run experiments using the latest ML tools and frameworks Develop automation tools to streamline research and system development Apply rigorous scientific methods to extract signals from complex datasets and shape our understanding of market behavior Partner with engineering teams to implement and test models in production environments

What we're looking for We’re looking for research scientists with a proven track record of applying deep learning to solve complex, high-impact problems. The ideal candidate will have a strong grasp of diverse machine learning techniques and a passion for experimenting with model architectures, feature engineering, and hyperparameter tuning to produce resilient and high-performing models.

PhD in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field Strong track record of applying ML in academic or industry settings, with 5+ years of experience building impactful deep learning systems A strong publication record in top-tier conferences such as NeurIPS, ICML, or ICLR Strong programming skills in Python and/or C++ Practical knowledge of ML libraries and frameworks, such as PyTorch or TensorFlow, especially in production environments Hands-on experience applying deep learning on time series data Strong foundation in mathematics, statistics, and algorithm design Excellent problem-solving skills with a creative, research-driven mindset Demonstrated ability to work collaboratively in team-oriented environments A passion for solving complex problems and a drive to innovate in a fast-paced, competitive environment

Johns Hopkins University

The Department of Biostatistics is seeking outstanding colleagues to join our tenure track faculty at the assistant professor level. We seek candidates to strengthen us in advancing statistical and data science, making discoveries to improve health, and providing an innovative biostatistics education. Responsibilities include methodological and collaborative research, teaching, and mentorship of graduate students. We are particularly interested in candidates with expertise in biostatistics, data science, and AI and a passion for public health. Johns Hopkins University has recently made a transformative investment launching a Data Science and AI institute that will serve the hub for interdisciplinary data collaborations with faculties and students from across Johns Hopkins and will build the nation’s foremost destination for emerging applications, opportunities and challenges presented by data science, machine learning and AI. We anticipate the individual hired into this position will have strong links with the Data Science and AI Institute.

Location UC Berkeley, Berkeley, CA US

Description The Bakar Institute of Digital Materials for the Planet (BIDMaP) is an institute in UC Berkeley’s new College of Computing, Data Science, and Society (CDSS), bringing together AI, machine learning and data science with the natural sciences to address the planet’s most urgent challenges. BIDMaP is focused on developing new techniques in AI that will enhance and accelerate discovery in experimental natural sciences and development of novel materials to address planetary challenges. To this end, BIDMaP promotes collaboration between world-renowned AI/ML experts, chemists, physicists and other physical scientists. By combining cutting-edge chemistry with artificial intelligence, machine learning, and robotics, BIDMaP is reimagining how materials can be designed and optimized for clean energy, clean air, clean water, advanced batteries, and sustainable chemical production.

Location CAN, ON, Toronto

Description Are you a passionate scientist in the computer vision area who is aspired to apply your skills to bring value to millions of customers? Here at Ring, we have a unique opportunity to innovate and see how the results of our work improve the lives of millions of people and make neighborhoods safer.

As a Principal Applied Scientist, you will work with talented peers pushing the frontier of computer vision and machine learning technology to deliver the best experience for our neighbors.

This is a great opportunity for you to innovate in this space by developing highly optimized algorithms that will work at scale. This position requires experience with developing Computer Vision, Multi-modal LLMs and/or Vision Language Models. You will collaborate with different Amazon teams to make informed decisions on the best practices in machine learning to build highly-optimized integrated hardware and software platforms.

AI Scientist

The Role

This AI Scientist position will drive the development and optimization of Aizen's generative AI-based peptide drug discovery platform, DaX™. You will be responsible for incorporating state-of-the-art neural network architectures and high-performance computational biology software to improve the accuracy and throughput of our drug discovery efforts. Your work will be critical in translating experimental data and scientific insights into scalable, robust models.

Our Ideal Candidate

You are passionate about the company’s mission and a self-starter with an inextinguishable fire to compete and succeed. You thrive in an environment that requires crisp judgment, pragmatic decision-making, rapid course-corrections, and comfort with market ambiguity. You discharge your duties within a culture of mutual team respect, high performance, humility, and humor.

Key Responsibilities

• Incorporate state-of-the-art neural network architectures and training methods to improve accuracy and throughput of DaX™, Aizen's generative AI-based peptide drug discovery platform.
• Develop, test, deploy, and maintain high-performance computational biology software according to the needs and feedback of experimentalists at Aizen.
• Orchestrate new and existing Aizen software tools into scalable, highly-available, and easy-to-use cloud pipelines.
• Work closely with experimental scientists at Aizen to manage storage and access of Aizen's experimental data.

Candidate Skills and Experience

• Ph.D. and/or postdoctoral studies in Computer Science, Computational Biology, Bioinformatics, or a related field.
• Deep, demonstrated expertise in advanced Generative Models (e.g., Flow Matching, Diffusion Models) for de novo design in discrete and continuous spaces.
• Experience integrating and leveraging data from physics-based simulations (e.g., Molecular Dynamics) into machine learning models.
• Experience collecting, sanitizing, and training on biological property datasets, with a preference for prior experience with peptides.
• Proficiency with Python, shell scripting, and a high-performance compiled language.
• Entrepreneurial spirit, self-starter with proper balance of scientific creativity and disciplined execution.
• Preferred: Experience designing and maintaining high-availability cloud architectures for hosting high-performance biological analysis software.
• Preferred: Experience in chemical featurization, representation, and model application for peptide chemistry, non-canonical amino acids (NCAAs), and complex peptide macrocycles.
• Preferred: Experience in protein/peptide folding dynamics, protein structural analysis, and resultant data integration to improve computation/design.

About Aizen

Aizen is an AI-driven biotechnology company pioneering Mirror Peptides, a novel class of biologic medicines. Mirror Peptides are synthetic, fully D-amino acid peptides that represent a vast, unexplored therapeutic chemical space. Backed by life science venture capital and based in the biotech hub of San Diego, CA.

Location & Compensation

• Reporting: Principal AI Scientist
• Location: This position offers fully remote work with monthly/quarterly trips to company facilities in California.
• Compensation: Competitive base salary, stock options, and a benefits package including medical coverage.

Contact

To apply, please contact us at jobs@aizentx.com.

An equal opportunity employer V1

Toronto or remote

Mission: We are seeking a highly skilled Machine Learning Engineer to join our advanced model development team. This role focuses on pre-training, continued training, and post-training of models, with a particular emphasis on draft model optimization for speculative decoding and quantization-aware training (QAT). The ideal candidate has deep experience with training methodologies, open-weight models, and performance-tuning for inference.

Responsibilities & opportunities in this role: Lead pre-training and post-training efforts for draft models tailored to speculative decoding architectures. Conduct continued training and post-training of open-weight models for non-draft (standard) inference scenarios. Implement and optimize quantization-aware training pipelines to enable low-precision inference with minimal accuracy loss. Collaborate with model architecture, inference, and systems teams to evaluate model readiness across training and deployment stages. Develop tooling and evaluation metrics for training effectiveness, draft model fidelity, and speculative hit-rate optimization. Contribute to experimental designs for novel training regimes and speculative decoding strategies.

Ideal candidates have/are: 5+ years of experience in machine learning, with a strong focus on model training. Proven experience with transformer-based architectures (e.g., LLaMA, Mistral, Gemma). Deep understanding of speculative decoding and draft model usage. Hands-on experience with quantization-aware training, including PyTorch QAT workflows or similar frameworks. Familiarity with open-weight foundation models and continued/pre-training techniques. Proficient in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.

Preferred Qualifications: Experience optimizing models for fast inference and sampling in production environments. Exposure to distributed training, low-level kernel optimizations, and inference-time system constraints. Publications or contributions to open-source ML projects.

Attributes of a Groqster: Humility - Egos are checked at the door Collaborative & Team Savvy - We make up the smartest person in the room, together Growth & Giver Mindset - Learn it all versus know it all, we share knowledge generously Curious & Innovative - Take a creative approach to projects, problems, and design Passion, Grit, & Boldness - no limit thinking, fueling informed risk taking

Successful hires will expand the group's efforts applying machine learning to drug discovery, biomolecular simulation, and biophysics. Areas of focus include generative models to help identify novel molecules for drug discovery targets, predict PK and ADME properties of small molecules, develop more accurate approaches for molecular simulations, and understand disease mechanisms. Ideal candidates will have strong Python programming skills. Relevant areas of experience might include deep learning techniques, systems software, high performance computation, numerical algorithms, data science, cheminformatics, medicinal chemistry, structural biology, molecular physics, and/or quantum chemistry, but specific knowledge of any of these areas is less critical than intellectual curiosity, versatility, and a track record of achievement and innovation in the field of machine learning. For more information, visit www.DEShawResearch.com.

Please apply using this link: https://apply.deshawresearch.com/careers/Register?pipelineId=597&source=NeurIPS_1

The expected annual base salary for this position is USD 300,000 - USD 800,000. Our compensation package also includes variable compensation in the form of sign-on and year-end bonuses, and generous benefits, including relocation and immigration assistance. The applicable annual base salary paid to a successful applicant will be determined based on multiple factors including the nature and extent of prior experience and educational background. We follow a hybrid work schedule, in which employees work from the office on Tuesday through Thursday, and have the option of working from home on Monday and Friday.

D. E. Shaw Research, LLC is an equal opportunity employer.