At Anduril Australia we are developing un-crewed maritime and air domain systems that leverage unsupervised autonomy for the delivery of long endurance, multi-mission capability to our customers. These projects, including but not limited to the Extra Large Autonomous Undersea Vehicle (XL-AUV) Program, require a Senior Machine Learning Engineer with deep experience developing, testing, validating and deploying machine-learning based sensor processing and the technical toolchains they rely on. The Senior Machine Learning Engineer strives to leverage their experience to rapidly architect, design, deliver, support, and evolve next generation autonomous vehicles through the entire product life-cycle.

WHAT YOU'LL DO

• Lead the design, development, integration and support of machine learning models for perception and data pipelines for a variety of autonomous systems across multiple sensor modalities
• Develop software to manage and automate the machine learning lifecycle, including data management, pipelining, labeling and training
• Manage model selection, training, validation, evaluation, deployment and regression testing
• Manage training data sourcing and integration, and quality
• Develop tools and processes for machine learning in a classified environment
• Collaborate with cross-functional teams, including software engineers, mechanical engineers, and systems engineers, to ensure effective system integration and testing

REQUIRED QUALIFICATIONS

• Bachelor’s degree in Robotics, Mechatronics, Computer Science, Engineering, a relevant field, or equivalent experience
• Experience in a senior role with the delivery of a production grade software system with a mature machine learning component
• Experience with the design, implementation and maintenance of ML Operations, including data acquisition, labeling, data curation, pipeline management, continuous integration, model versioning, and monitoring
• Ability to collaborate with data scientists and stakeholders to define and implement robust validation and verification strategies
• Experience with the delivery of computer vision capabilities, from the data side as well as the model side
• Capacity to work holistically on machine learning enabled capabilities up and down the software stack and through lifecycle through design, implementation, operation and sustainment
• Capacity to learn and grow individually, while mentoring junior team members effectively, building team cohesion and capacity
• Eligible to obtain and maintain an Australian Government Negative Vetting 2 security clearance (NV2) 

PREFERRED QUALIFICATIONS

• Experience with the delivery of capability with a public sector customer
• Experience with workflow management tools such as flyte, airflow, and kubeflow
• Experience with deep learning frameworks such as PyTorch
• Experience with edge ML applications
• Experience with inference engines such as TensorRT
• Experience writing C++, Go, Python or Rust
• Proficiency in data management and traceability techniques to maintain high-quality datasets and enable reproducibility of experiments and results
• Desire to grow in to a Tech-Lead-Manager role, with responsibility for line management of engineers, in addition to delivery

Anduril is an equal-opportunity employer, and we encourage candidates from all backgrounds to apply. If you are someone passionate to work on problems that matter, we’d love to hear from you!

CSQ227R47

The AI Forward Deployed Engineering (AI FDE) team is a highly specialized customer-facing AI team at Databricks. We deliver professional services engagements to help our customers build and productionize first-of-its-kind AI applications. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams. We view our team as an ensemble: we look for individuals with strong, unique specializations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in GenAI, LLMOps, and ML more broadly. Open to remote locations.

The impact you will have:

• Develop cutting-edge GenAI solutions, incorporating the latest techniques from our Mosaic AI Research to solve customer problems
• Own production rollouts of consumer and internally facing GenAI applications
• Serve as a trusted technical advisor to customers across a variety of domains
• Present at conferences such as Data + AI Summit, recognized as a thought leader internally and externally
• Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap

What we look for:

• Experience building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine-tuning, etc., with tools such as HuggingFace, LangChain, and DSPy
• Expertise in deploying production-grade GenAI applications, including evaluation and optimizations 
• Extensive years of hands-on industry data science experience, leveraging common machine learning and data science tools, i.e. pandas, scikit-learn, PyTorch, etc.
• Experience building production-grade machine learning deployments on AWS, Azure, or GCP
• Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
• Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike
• Passion for collaboration, life-long learning, and driving business value through AI
• [Preferred] Experience using the Databricks Intelligence Platform and Apache Spark™ to process large-scale distributed datasets

As a Machine Learning Engineer, you will play a pivotal role in building systems that drive the training and deployment of large-scale ML models across our global operations. You'll collaborate with leading researchers, hardware experts, and software engineers to build robust solutions that maximize the potential of GPU acceleration, distributed computing, and the latest open-source tools. Your work will influence our trading strategies by accelerating experimentation cycles that foster continuous innovation and refinement.

This is a unique opportunity to solve problems at the intersection of advanced machine learning and trading, where your contributions will shape the future of IMC’s technology and trading capabilities.

Your Core Responsibilities: 

• Develop large-scale distributed training pipelines to manage datasets and complex models
• Build and optimize low-latency inference pipelines, ensuring models deliver real-time predictions in production systems
• Develop libraries to improve the performance of machine learning frameworks
• Maximize performance in training and inference using GPU hardware and acceleration libraries
• Design scalable model frameworks capable of handling high-volume trading data and delivering real-time, high-accuracy predictions
• Collaborate with quantitative researchers to automate ML experiments, hyperparameter tuning, and model retraining
• Partner with HPC specialists to optimize workflows, improve training speed, and reduce costs
• Evaluate and roll out third-party tools to enhance model development, training, and inference capabilities
• Dig into the internals of open-source ML tools to extend their capabilities and improve performance

Your Skills and Experience: 

• 5+ years of experience in machine learning with a focus on training or inference systems
• Hands-on experience with real-time, low-latency ML pipelines in high-performance environments is a strong plus
• Strong engineering skills, including Python, CUDA, or C++
• Knowledge of machine learning frameworks such as PyTorch, TensorFlow, or JAX
• Proficiency in GPU programming for training and inference acceleration (e.g., CuDNN, TensorRT)
• Experience with distributed training for scaling ML workloads (e.g., Horovod, NCCL)
• Exposure to cloud platforms and orchestration tools
• A track record of contributing to open-source projects in machine learning, data science, or distributed systems is a plus

#LI-DNP

At IMC, we believe technology is the foundation of our competitive edge — and machine learning is increasingly central to how we trade. Over the past few years, we've been steadily building our machine learning capabilities: developing infrastructure, growing our in-house GPU cluster, deploying models into production, and partnering closely with quant researchers and traders to generate real impact. Now we’re expanding the team, scaling our systems, and accelerating the application of deep learning in our research and execution workflows.  We're looking for a Principal Machine Learning Engineer to help shape the next phase of our platform — influencing architecture, driving best practices, and solving high-leverage problems. You’ll work alongside researchers and technologists to design the systems that power experimentation, training, and deployment of ML models — and help set the direction for how machine learning is done at IMC as we scale. If you’ve built ML infrastructure at scale elsewhere and are looking for a role where your ideas will genuinely help shape our firm’s future — we’d love to hear from you.

Your Core Responsibilities: 

• Design and build end-to-end infrastructure for training, evaluation, and productionization of ML models, working closely with our HPC engineers who manage our on-prem compute cluster
• Influence foundational choices around data access, compute orchestration, experiment tracking, model versioning, and deployment pipelines
• Partner with quant researchers to accelerate iteration cycles, tighten feedback loops, and bring models from prototype to live trading
• Work with researchers to adapt and deploy modern architectures — transformers, state-space models, temporal convolutions, graph neural networks — to noisy, high-frequency financial data. Explore techniques like self-supervised pretraining, representation learning, and cross-sectional modelling where they offer genuine edge
• Shape our approach to reproducibility, continual learning, and production monitoring across a petabyte-scale data environment
• Define standards that create consistency across teams and geographies; mentor engineers and influence technical culture beyond your immediate work
• Keep pace with developments in deep learning research and ML infrastructure; bring ideas from academia and industry into how we work — whether that's new architectures, training techniques, or tooling

Your Skills and Experience: 

• 8+ years of experience building ML platforms or infrastructure at a leading tech company, research lab, or quantitative firm
• A track record of designing and owning large-scale training and inference systems — not just contributing, but architecting
• Deep proficiency in Python, with strong experience in either CUDA or C++
• Hands-on expertise with modern deep learning frameworks (PyTorch, TensorFlow, or JAX) and practical experience implementing architectures like transformers, attention mechanisms, or sequence models
• Strong foundation in deep learning fundamentals: optimization, regularization, loss design, and the trade-offs that matter when training at scale
• Experience with distributed training at scale (Horovod, NCCL) and GPU optimization (cuDNN, TensorRT)
• History of deploying models to production with strong observability, reproducibility, and monitoring practices
• Comfort working across the ML stack from data pipelines to training infrastructure to serving systems

Why This Role: 

• Build, don't inherit — You'll make foundational technology choices in a platform that's still being defined, not maintain someone else's legacy.
• Real investment, real backing — This is a strategic priority with resources behind it, not a side experiment.
• Direct impact on trading — Your infrastructure will power models that make real trading decisions in competitive global markets.
• Global scope — Work with teams across New York, Chicago, Amsterdam, London, Sydney, Hong Kong and beyond; define practices that can scale worldwide.
• Ideas over titles — IMC's culture values clarity, rigor, and collaboration. The best ideas win, regardless of where they come from.
• Tight coupling with research — You won't be building in isolation. Researchers and engineers work side-by-side, iterating together.

#LI-DNP

IMC is looking for experienced quant researchers to develop high to mid frequency delta one trading strategies and predictive models for the APAC markets. If you’re excited about helping to push the boundaries of what we can do with Machine Learning in trading, unlocking the significant edges we have in execution, and collaborating to become the best trading firm worldwide, this may be the role for you.

You will be responsible for performing large scale data analysis to derive statistically profitable predictions of market behaviour. These predictions are used to inform all of our trading, and improvements have a high and visible impact across the office. You will also help to shape the direction we take across research and tooling. We have longstanding and significant edges across market access, global reach, Options understanding and low latency. The rapid growth we’ve already seen in Machine Learning has unlocked these edges, and some of the most interesting and impactful problems are now being tackled.

You will work as part of an established and growing research team, collaborating closely with traders, software and hardware developers to find improvements to our models and see them impact our production results. IMC competes and wins as a team, with open idea sharing and collaboration across disciplines, desks and offices.

Your Core Responsibilities:

• Leverage deep learning techniques to improve prediction quality in a trading context
• Collaborate with feature engineers and traders, to help ensure the feature set is crafted and used to maximum effect
• Develop and improve sampling, weighting, cost, target, hyperparameters and network architecture to suit a range of problems and data presentations
• Push the team forward, by keeping abreast of the latest developments in industry and academia, and tackling some of the hardest problems we have in the quant space
• Mentor and grow the skills of more junior colleagues in the space, and clearly and simply explain complex concepts
• Apply understanding of distributed computing, and how to align training hardware and approach to rapidly achieve strong results on very large datasets

Your Skills and Experience:

• Graduate & Postgraduate study from top universities, majoring in machine learning, statistics, or STEM subjects
• 3+ years experience as a quantitative modeller, with specific experience in the high to mid frequency delta one space. Have a proven track record of developing and improving deep learning models with proven outperformance in production
• Strong programming experience in at least one language, and mainstream deep learning framework (TensorFlow, PyTorch or other)
• Deep understanding of the strengths & weaknesses of CNN, RNN, LSTM and transformers