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!

We are seeking an innovative and experienced Embodied AI Researcher, focusing on the development and application of foundation models in robotics. The successful candidate will contribute to cutting-edge research, designing models and experiments that advance the field of robotics through deep learning and zero-shot generalization.

Responsibilities:

• Train foundation models for robotics, utilizing architectures similar to RT-X.
• Align text-based reasoning processes with low-level robotic control policies.
• Design and conduct experiments to evaluate zero-shot generalization in robotic systems.
• Collaborate with interdisciplinary teams to integrate AI models into practical robotic applications.
• Document and present research findings at internal and external conferences.

Qualifications:

• PhD or equivalent research experience in Deep Learning and Robotics.
• Proven track record of publications in top-tier conferences such as CoRL, ICRA, and NeurIPS.
• Practical experience with imitation learning and behavior cloning techniques.
• Strong programming skills in Python and familiarity with machine learning frameworks such as TensorFlow or PyTorch.
• Excellent problem-solving skills and the ability to work independently and as part of a team.

We are seeking a highly skilled LLM Pre-training & Distributed Systems Engineer. This role is essential for orchestrating large-scale machine learning training runs and optimizing  distributed infrastructure. The ideal candidate will have a deep understanding of GPU clusters and extensive experience in system engineering to ensure efficient and reliable training processes.

Responsibilities:

• Orchestrate distributed training runs across 1,000+ GPUs using PyTorch, DeepSpeed, or Megatron-LM.
• Optimize networking (InfiniBand/RDMA) and memory management to prevent out-of-memory errors.
• Automate checkpointing and failure recovery during month-long training runs.

Required Skills:

• Deep expertise in 3D parallelism (Data, Tensor, Pipeline).
• Experience managing SLURM or Kubernetes-based GPU clusters.
• Strong systems engineering background (C++, CUDA, Python).

Passionate about unearthing and driving complex data science solutions across huge amounts of data to drive business value? Join our growing Data Science Team!

What's in it for you?

As a Senior Data Scientist, you will play a pivotal role in driving all stages of our data science projects including: ideation, scoping, development, deployment and monitoring. Working closely with business stakeholders and leadership, you will get to work on our global platforms to architect and implement scalable solutions that will impact millions of users worldwide.

Your role with us:

In your role with us, you will drive and technically lead complex machine learning and AI initiatives across our global platform, architecting and implementing scalable solutions that handle petabyte-scale data and impact millions of users worldwide. The role involves providing technical leadership, mentoring junior team members, and working closely with stakeholders to deliver high-impact data science solutions that drive business value.

Who are we?

At Easygo we proudly stand as a prominent service provider to a powerhouse of brands within the entertainment industry, including Stake.com, Kick.com and Twist Gaming. 

Stake is the world's largest crypto casino, and leads the industry with a seamless online casino and sportsbook experience.

Level up your online entertainment with Kick.com, the vibrant live-streaming platform, which connects millions of gamers and content creators worldwide.

All alongside the innovative game design studio, Twist Gaming, which takes creativity to new heights by crafting cutting-edge and captivating games. 

Our commitment to placing our clients and their communities' entertainment at the forefront of everything we do, has solidified us as the ultimate online service provider for entertainment companies.

Headquartered in the beautiful city of Melbourne, our growth has been remarkable. From humble beginnings to a thriving workforce of 700+, we've expanded not only in numbers but in ambition. There really is something for everyone here, whether you work in Tech, Marketing, Operations, Mathematics or Design, we are sure to have something for everyone.

Click play, on your career today!

What you will do:

• Technically lead the end-to-end machine learning solutions. Design, develop, and deploy comprehensive machine learning models from ideation and data collection to deployment and ongoing monitoring.
• Drive the technical strategy and architecture decisions for large-scale data science projects
• Apply statistical analysis and machine learning techniques to solve key business problems across various domains.
• Design and implement robust monitoring systems for production ML models.
• Partner with stakeholders to understand needs and communicate complex data science concepts, progress, and results to both technical and non-technical audiences.
• Mentor and provide technical guidance to junior data scientists.

What you will bring: 

• 5+ years of hands-on data science experience in industry.
• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or related quantitative field.
• Expert-level programming skills in the Python ecosystem (numpy, pandas, scikit-learn).
• Advanced proficiency in SQL.
• Demonstrated expertise in machine learning algorithms and their practical applications.
• Proven experience in designing and deploying production ML systems.
• Experience with cloud platforms and distributed computing.
• Track record of successfully delivering end-to-end ML projects.

Bonus points if you also have:

• Master's or PhD in relevant quantitative discipline.
• Deep expertise in AWS cloud services and ML deployment pipelines.
• Extensive experience with big data technologies and distributed computing frameworks.
• Specialized expertise in one or more areas: NLP, Computer Vision, Recommender Systems
• Experience with deep learning frameworks, particularly PyTorch.
• Experience leading technical teams or mentoring junior data scientists.
• Experience with ML systems design for high-scale, real-time applications.

Some of the perks of joining us:

• Access to over 9,000 courses across our Learning and Development Platform.
• EAP access for you and your family.
• Be rewarded with lucrative annual bonuses.
• Give back with a paid volunteer day.
• Fuel your day with daily breakfast and open pantries brimming with unlimited snacks and refreshments, all on the house.
• Break up the week with on site remedial massage Wednesdays.
• In house full-time barista’s providing you your daily coffee needs.
• Weekly team lunches and happy hour in the office from 4pm on Fridays.
• Enjoy a bustling office with the option for up to 2 days work from home per week.
• Fun office environment with F1 simulators, table tennis and all your favourite gaming consoles.

We believe that the unique contributions of everyone at Easygo are the driver of our success. To make sure that our products and culture continue to incorporate everyone's perspectives and experience we never discriminate on the basis of race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. We are passionate about providing a workplace that encourages great participation and an equal playing field, where merit and accomplishment are the only criteria for success.

About the AI Research Team

The AI Research team at Algolia combines fundamental research with product engineering to deliver customer-facing AI-powered features.

The team is highly cross-functional, made up of PhD researchers, full-stack engineers, and infrastructure specialists working together to explore new ideas, validate impact, and bring successful research outcomes into production. While the work is research-driven, the output is real, customer-facing systems.

The Opportunity

We are looking for an embedded Senior Site Reliability Engineer to join the AI Research team as a full member of the group. In this role, you will support both the research and product-engineering aspects of the team by ensuring the stability, scalability, and operability of the infrastructure that enables this work.

This is a classic SRE role focused on cloud-first, service-oriented architectures running on Google Cloud Platform. While the team builds AI-powered systems, AI or ML experience is not required for this role. Our priority is strong SRE fundamentals, experience operating production services, and comfort working in an environment with ambiguity and high ownership.

You will play an important role in day-to-day execution as well as in longer-term (12-month) planning, helping shape how the team builds and operates its platforms over time.

What You’ll Work On

Platform Reliability & Enablement

• Support and evolve the reliability of platforms used by the AI Research team. Examples of our infrastructure work to date include:
• A production inference service (embedding model serving API)
• AI data feature store
• Internal tools used for novel research and experimentation
• Infrastructure that combines the above to enable offline testing of customer deployments to agentically discover configuration improvements.
  
  
• Ensure production services meet expectations for availability, latency, and operational readiness, particularly for systems that sit on customer-critical paths
  
  
• Design infrastructure and operational patterns that prioritize iteration speed while maintaining appropriate safeguards for production systems
  
  

Embedded Collaboration

• Work closely with researchers and engineers in a cross-functional setting, acting as an advisor on infrastructure, reliability, and operational concerns
  
  
• Participate directly in team planning and execution, from early exploration through production rollout
  
  
• Help researchers self-serve infrastructure safely and effectively, without becoming a bottleneck
  
  

Cloud Infrastructure & Operations

• Build and maintain Kubernetes-based services on GCP using infrastructure-as-code and GitOps (Terraform, ArgoCD)
  
  
• Own and improve CI/CD pipelines for services written primarily in Go, with some Python-based services
  
  
• Design and operate observability systems using tools such as Datadog
  
  
• Participate in an on-call rotation (relatively light), responding to incidents and helping improve systems over time
  
  

What We’re Looking For

Required Experience

• Strong experience operating cloud-first infrastructure
  
  
• Hands-on experience running production services on Kubernetes
  
  
• Proficiency with infrastructure-as-code (Terraform) and CI/CD systems
  
  
• Experience supporting production services written in Go (Python experience is a plus)
  
  
• Solid grounding in service reliability, incident response, and operational best practices
  
  
• Comfort working in environments with ambiguity, where problems are not always well-defined upfront
  
  

Nice to Have

• Experience supporting mission-critical internal platforms
  
  
• Exposure to research or experimentation-heavy environments
  
  
• Familiarity working alongside researchers or highly specialized domain experts
  
  

Explicitly Not Required

• AI, ML, or deep learning experience
  
  
• Model training, tuning, or ML framework expertise (e.g. PyTorch, JAX)
  
  

Ways This Role May Not Be a Fit

This role may not be a good match if:

• You are only interested in maintaining existing infrastructure without contributing to what is being built
  
  
• You want to work exclusively on customer-facing product features
  
  
• You are looking to avoid on-call or production systems entirely
  
  
• You are seeking narrowly defined work with low ambiguity and limited ownership
  
  
• You want to build or train AI models yourself rather than enable the systems around them
  
  

Why Join the AI Research Team

• High Impact: Your work directly enables new AI-powered capabilities that reach customers
  
  
• High Agency: You’ll help shape what gets built, how it’s built, and whether it’s worth building
  
  
• Strong Peers: Collaborate with experienced SREs, engineers, and PhD researchers
  
  
• Growth: Build expertise in research-adjacent infrastructure and platform reliability
  
  
• Flexibility: Australia-based role with remote-friendly culture; occasional off-hours collaboration may be required
  
  

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

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