POSITION SUMMARY

CIQ is seeking a highly experienced Senior or Principal AI Engineer to own and drive AI/ML innovation across our product portfolio. This role sits at the intersection of AI engineering and systems performance - the right candidate brings deep expertise in model inference optimization, training workflows, and production AI deployment, combined with a strong instinct for performance at the systems level.

In this role, you will be the AI engineering standard-bearer at CIQ. You will design and build turnkey AI workload examples - both internal reference pipelines and customer-facing solutions - ensuring that CIQ’s AI story is always compelling, practical, and demonstrably best-in-class. You will integrate deeply with Fuzzball, CIQ’s cloud-native computing platform, running AI workloads end-to-end through it and helping customers do the same.

KEY RESPONSIBILITIES

This role is leveled as Senior or Principal based on qualifications and demonstrated capabilities.

AI Inference Optimization

• Design, implement, and tune inference pipelines for large language models and other AI workloads, targeting maximum throughput and minimum latency.
• Apply state-of-the-art optimization techniques: quantization (INT4/INT8/FP8), model pruning, speculative decoding, continuous batching, and kernel fusion.
• Optimize inference-serving stacks, including vLLM, TensorRT-LLM, ONNX Runtime, and similar frameworks, for production deployment on CIQ’s OS platform.
• Profile and tune GPU/accelerator utilization across the full inference stack, from model weights and memory bandwidth to CUDA kernels and driver overhead.
• Establish inference performance baselines and regression detection across CIQ’s AI-focused solutions.

AI Training Workflows

• Design and optimize distributed training pipelines for large-scale models, including data, model, tensor, and pipeline parallelism strategies.
• Tune training efficiency through mixed-precision training, gradient checkpointing, activation recomputation, and optimizer-level improvements.
• Benchmark training throughput and scaling efficiency across multi-GPU and multi-node configurations on CIQ’s infrastructure.
• Collaborate with infrastructure and performance teams to resolve training bottlenecks at the network (RDMA/InfiniBand), storage, and OS layers.
• Stay current on frontier model architectures and training techniques, including MoE models, RLHF pipelines, and emerging post-training methods.

Turn-Key AI Examples & Reference Workloads

• Build and maintain a library of turn-key AI workload examples that run on CIQ’s platform, covering inference serving, fine-tuning, batch processing, RAG pipelines, and agentic workflows.
• Develop both internal reference pipelines for CI/testing and customer-facing examples designed for immediate productivity on CIQ’s OS and Fuzzball.
• Package workloads using containers to deliver portable, reproducible AI environments across HPC and cloud-native settings.
• Create compelling, well-documented demos and reference architectures that communicate CIQ’s AI capabilities to technical and business audiences alike.
• Partner with product and customer success teams to translate real-world AI use cases into reusable, production-quality examples.

AI Engineering & Tooling

• Build and maintain AI-powered engineering tooling - leveraging LLM-based agents, automated analysis pipelines, and AI-assisted code generation to accelerate the broader engineering organization.
• Champion an AI-first development culture: identify opportunities where AI tooling can reduce toil, surface insights faster, and improve software quality across CIQ’s products.
• Evaluate and integrate emerging AI frameworks, libraries, and hardware as they become relevant to CIQ’s customers and product roadmap.
• Contribute to open-source AI tooling and frameworks where relevant, reinforcing CIQ’s technical reputation in the community.

Fuzzball Integration

• Develop deep expertise in CIQ’s Fuzzball platform, its architecture, scheduling model, and workload execution environment.
• Integrate AI training, inference, and pipeline workloads into Fuzzball-based CI/CD and production pipelines.
• Contribute to Fuzzball’s AI workload story: ensure the platform is a first-class environment for running AI workloads efficiently and at scale.
• Help characterize and improve Fuzzball’s performance for AI-specific access patterns and resource demands.

Cross-Functional Collaboration

• Develop broad familiarity with the full CIQ product portfolio, including Rocky Linux and RLC (and its variants), Fuzzball, Apptainer, and Warewulf, and understand how AI workloads interact with each layer.
• Collaborate closely with the Performance Engineering team to ensure AI workloads benefit from and contribute to CIQ’s systems-level optimization work.
• Partner with product and customer success teams to translate real-world AI pain points into engineering priorities and measurable outcomes.
• Document and communicate findings clearly, from low-level profiling data to executive-level summaries.
• Contribute to technical publications, conference presentations, and thought leadership that reinforces CIQ’s reputation as an AI-forward infrastructure company.

NEEDED TO SUCCEED

Successful candidates will have: 

• Deep, hands-on expertise in LLM inference optimization: including serving frameworks (vLLM, TensorRT-LLM, ONNX Runtime), quantization techniques, and GPU memory management.
• Strong background in distributed AI training, including frameworks such as PyTorch FSDP, DeepSpeed, Megatron-LM, or JAX/XLA.
• Proven experience building production AI pipelines and packaging AI environments for reproducible, portable deployment (containers, Apptainer/Singularity, or equivalent).
• Fluency with GPU/accelerator profiling tools: NVIDIA Nsight, PyTorch Profiler, CUDA performance analysis, and related tooling.
• Familiarity with HPC environments: job schedulers (Slurm, PBS), parallel filesystems, RDMA/InfiniBand, and MPI,  and the intersection of HPC with modern AI workloads.
• Experience integrating AI workloads into CI/CD pipelines and building automated testing and benchmarking frameworks.
• Comfort using and building with LLM-based tools and agentic frameworks to accelerate engineering work.
• Excellent analytical skills and able to form hypotheses, design experiments, and draw actionable conclusions from complex profiling data.
• Strong written and verbal communication skills; able to present findings to both deeply technical audiences and business stakeholders.
• A collaborative, humble, and always-learning mindset, combined with the confidence to champion AI engineering as a first-class concern.

EDUCATION AND EXPERIENCE

• PhD in Computer Science, Machine Learning, Computer Engineering, or a related field strongly preferred; equivalent industry experience considered.
• 10+ years of industry experience in AI/ML engineering, systems software, or a closely related discipline.
• Demonstrated track record of measurable, published, or production-deployed AI performance improvements at scale.
• Experience working in or with open-source AI ecosystems (PyTorch, Triton, ONNX, Hugging Face, etc.) is a strong plus.
• Background with cloud-native, containerized, and/or HPC computing environments preferred.

We’re seeking a Data & AI Engineer to develop intelligent data pipelines and analytics solutions that power smarter decisions across silicon design, verification, and manufacturing. You’ll transform engineering data into actionable insights through automation, modeling, and visualization.

Responsibilities

• Build and maintain data pipelines to support machine learning and analytics workflows.
• Collect, clean, and transform large, complex datasets from engineering environments.
• Develop and train predictive models for yield, performance, and anomaly detection.
• Automate recurring data analysis tasks and integrate models into engineering processes.
• Collaborate with design and software teams to embed AI-driven insights into products.
• Create dashboards and visualization tools for reporting and decision-making.
• Document code, models, and processes for transparency and reproducibility.

Qualifications

• Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Strong skills in data manipulation (Pandas, NumPy, SQL).
• Experience with workflow orchestration (Airflow, Spark, or similar).
• 3–5 years of experience in data engineering or applied AI.
• Bachelor’s degree in Electrical Engineering, Computer Science, or related field.

Preferred / Plus

• Familiarity with semiconductor design, verification, or manufacturing datasets.
• Understanding of statistical modeling and predictive maintenance.
• Experience with cloud environments (AWS, Azure, GCP) and version control (Git).
• Knowledge of MLOps principles (deployment, monitoring, CI/CD).

About the role:

We’re looking for a Senior Machine Learning Engineer to accelerate our AI research-to-production pipeline. You’ll build and improve the infrastructure that enables our research team to rapidly deploy and safely test new models, while helping ensure our production inference systems remain efficient, scalable, and reliable. You’ll identify gaps and opportunities in our ML infrastructure, scope solutions to ambiguous technical problems, and help set the technical direction for how we bridge research innovation and production reliability. This role requires a strong backend engineering background in distributed systems and containerization, and a track record of independently driving projects from concept to delivery. This is a cross-functional role that requires close collaboration with both research teams developing models and engineering teams supporting the broader platform.

What You’ll Do:

• Design and implement tooling that enables researchers to quickly deploy and evaluate new models in production 
• Design, build, and maintain high-performance, cost-efficient inference pipelines, making architectural decisions about scaling, reliability, and cost trade-offs
• Proactively identify and resolve infrastructure bottlenecks, proposing and scoping improvements to iteration speed and production reliability
• Develop and maintain user-facing APIs that interact with our ML systems
• Implement comprehensive observability solutions to monitor model performance and system health
• Troubleshoot and lead resolution of complex production issues across distributed systems, driving root-cause analysis and implementing preventive measures
• Set the direction for and continuously improve our MLOps practices, identifying the highest-impact opportunities to reduce friction between research and production.
• Collaborate closely with research and engineering teams to align on technical direction, and help onboard and mentor engineers on ML infrastructure best practices.

What You’ll Need:

• Strong backend engineering experience with Python
• Experience building and operating distributed, containerized applications, preferably on AWS 
• Proficiency implementing observability solutions (monitoring, logging, alerting, tracing) for production systems
• Ability to design and implement resilient, scalable architectures
• Track record of independently scoping and delivering complex technical projects from problem identification through production deployment
• Comfort navigating ambiguity and making pragmatic technical decisions when requirements are unclear or evolving

An ideal candidate should also have some of the following:

• MLOps experience, including familiarity with PyTorch and Kubernetes
• Experience working in fast-paced environments where you owned technical direction for an area and drove projects with minimal oversight.
• Experience collaborating with remote, globally distributed teams
• Comfort working across the entire ML lifecycle from model serving to API development
• Experience in audio-related domains (ASR, TTS, or other domains involving audio processing)
• Experience with other cloud providers
• Familiarity with Bazel and monorepos
• Experience with alternative ML inference frameworks beyond PyTorch
• Experience with other programming languages
• Experience mentoring junior engineers or onboarding teammates onto complex systems

Pay Transparency:

AssemblyAI strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity for our team. Our salary ranges are based on paying competitively for our size, stage, and industry, and are one part of many compensation, benefit, and other reward opportunities we provide.

There are many factors that go into salary determinations, including relevant experience, skill level, qualifications assessed during the interview process, and maintaining internal equity with peers on the team. The range shared below is a general expectation for the function as posted, but we are also open to considering candidates who may be more or less experienced than outlined in the job description. In this case, we will communicate any updates in the expected salary range.

The provided range is the expected salary for candidates in the U.S. Outside of those regions, there may be a change in the range which will be communicated to candidates throughout the interview process.

Salary range: $195,000 - $225,000

Research Scientist – Controlled 3D Generation

Location: Remote

About the Role

We’re seeking a Research Scientist passionate about 3D generation, flow matching, and diffusion models. You’ll help advance the frontier of controllable 3D content creation—building models that generate consistent, editable, and physically grounded 3D assets and scenes.

What You’ll Do

• Conduct cutting-edge research on flow-matching, diffusion, and score-based methods for 3D generation and reconstruction.
• Design and implement scalable training pipelines for controllable 3D generation (meshes, Gaussians, NeRFs, voxels, implicit fields).
• Develop techniques for conditioning and control (text, sketch, pose, camera, physics) and multi-view consistency.
• Analyse model behaviour through ablations, visualisations, and quantitative metrics.
• Collaborate with cross-disciplinary research, graphics, and infrastructure teams to translate research into production-ready systems.
• Publish results at top-tier venues and work with interns.

What You Bring

• PhD (or equivalent experience) in Machine Learning, Computer Vision, or Computer Graphics.
• Published work on diffusion, flow-matching, or score-based generative models (2D or 3D).
• Strong engineering and problem-solving abilities: experience with PyTorch, JAX, or CUDA-level optimisation.
• Understanding of 3D representations (meshes, Gaussians, signed-distance fields, volumetric grids, implicit networks).
• Solid grasp of geometry processing, multi-view consistency, and differentiable rendering.
• Ability to scale experiments efficiently and communicate complex results clearly.

Bonus / Preferred

• Experience generating coherent 3D scenes with multiple interacting objects, lighting, and spatial layout.
• Familiarity with scene-level control (object placement, camera path, simulation, or text-to-scene composition).
• Knowledge of video-to-3D, image-to-scene, or 4D temporal generation.
• Background in physically-based rendering, simulation, or world-model architectures.
• Track record of impactful publications or open-source releases.

Equal Employment Opportunity:

We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.

Bioinformatics Machine Learning Intern

RefinedScience | United States (hybrid or remote)

At RefinedScience, our mission is to advance care by bringing together the best science, data and minds – disease by disease, patient by patient, cell by cell to discover pathways to life beyond disease.

What We Are Looking For

We are seeking a highly motivated Bioinformatics Machine Learning Intern to join our team. This internship is designed for Ph.D. candidates with experience applying machine learning, deep learning, or generative AI methods to single-cell omics data. You will contribute to active projects spanning single-cell biology, multiomics integration, and computational approaches to precision medicine and drug development.

Our Bioinformatics team plays a crucial role in integrating computational biology, large-scale data analysis, and machine learning to drive discoveries in precision medicine and drug development.

Key Activities

• Analyze single-cell and multiomics datasets to extract biological insights supporting precision medicine and drug development programs
• Apply and evaluate machine learning and deep learning approaches to single-cell data for tasks such as cell type classification, biomarker discovery, and patient stratification
• Explore and prototype generative AI and LLM-based approaches to accelerate biological data interpretation and scientific workflows
• Collaborate with scientists, clinicians, and data scientists to design and execute data-driven research projects
• Document and optimize computational workflows following reproducible research best practices
• Present findings through technical reports, visualizations, and presentations to cross-functional teams

Must Haves

• Current Ph.D. candidate in Bioinformatics, Computational Biology, Computer Science, Biostatistics, or a related quantitative field
• Single-cell omics experience: Demonstrated ability to process, analyze, and interpret single-cell data (scRNA-seq, scATAC-seq, CITE-seq, or spatial transcriptomics) using frameworks such as Scanpy/scverse, Seurat, or Bioconductor
• Machine learning expertise: Applied experience developing and evaluating ML/deep learning models on biological data, including neural network architectures (GNNs, transformers, autoencoders), model selection and benchmarking, and integration of ML approaches into analytical workflows
• Programming proficiency: Python and/or R for data analysis, statistical modeling, and visualization
• Statistical foundation: Understanding of statistical methods for biological data (hypothesis testing, differential expression, multiple testing correction, clustering)
• Strong problem-solving skills and ability to communicate complex insights effectively

Desired Qualifications

Machine Learning & AI

• Experience with deep learning frameworks (PyTorch, TensorFlow, JAX)
• Familiarity with graph neural networks, attention mechanisms, or transformer architectures applied to biological data
• Experience with ML experiment tracking and reproducibility (MLflow, Weights & Biases)
• Exposure to representation learning, variational autoencoders, or contrastive learning methods
• Familiarity with scikit-learn, XGBoost, or similar ML libraries
• Interest in or experience with LLMs, RAG systems, or agentic AI tooling

Bioinformatics

• Experience with multimodal single-cell integration (Seurat WNN, scvi-tools/MultiVI/totalVI, Muon)
• Familiarity with spatial transcriptomics analysis (Squidpy, cell2location, nf-core/spatialvi)
• Experience with cell-cell communication inference (CellChat, NicheNet, LIANA)

• Knowledge of drug-gene interaction resources (CMap/LINCS, OpenTargets, ChEMBL)

Engineering & Infrastructure

• Familiarity with Linux/Unix CLI and version control (Git/GitHub)
• Experience with containerization (Docker, Singularity) and environment management (conda, venv)
• Exposure to cloud computing platforms (GCP preferred)
• Familiarity with workflow managers (Nextflow, Snakemake)
• Adherence to best-practices for conduct reproducible computational research

Duration

8–10 weeks

Why You'll Love RefinedScience

Team + Values

At RefinedScience, we seamlessly integrate top-tier clinical and biological data with expert knowledge to provide unparalleled insights. We maximize patient impact with these unique insights by optimizing clinical trial probability of success and time to actionable results. We work across biopharma and we are a trusted partner in achieving better results, faster – working together to unlock strategic advantage.

Our Values

• Act with Purpose – We believe in rigor through deliberate and thoughtful actions
• Be Curious – Curiosity is the spark that ignites innovation and growth
• Take Ownership – True ownership leads to pride and commitment in the work we do
• Invest in Relationships – Building strong connections is the foundation for effective collaboration and trust for long term success
• Embrace Agility – We celebrate agile thinking, resilience, and adaptability

Compensation

• $34-$38 per hour

Location: Remote

Duration: 2–4 months (project-based)

Type: Contract / Research Collaboration (Paid)

About the Project

We are looking for a Master’s or PhD student to work on fine-tuning large language models (LLMs) for domain-specific tasks. The goal is to take an existing pretrained model (e.g., Meta AI’s LLaMA-class models or similar) and specialize it for a narrow, high-value use case using efficient fine-tuning techniques.

This is a hands-on applied project designed for someone who wants real-world experience deploying and optimising LLM systems.

Help drive the next wave of applied AI by demonstrating how fine-tuned LLMs can unlock advanced, real-world use cases beyond general-purpose foundation models. Organizations that require domain-specific accuracy, self-hosted deployments, customisable workflows, or performance beyond out-of-the-box capabilities increasingly rely on fine-tuned models to meet those needs.

Through this project, you will contribute to building specialised AI systems that deliver improved accuracy, efficiency, and control compared to out-of-the-box models. You will also help bridge the gap between academic knowledge and real-world application by applying fine-tuning techniques to solve concrete business problems.

What You’ll Work On

• Fine-tuning pre-trained LLMs on small to medium datasets (500–20k examples)
• Implementing parameter-efficient fine-tuning (e.g., LoRA-style methods)
• Optimising training for cost and performance
• Running experiments on GPU cloud infrastructure
• Evaluating model performance and tradeoffs (specialisation vs generalisation)
• Deploying fine-tuned models for inference

Experience

• Strong Python skills
• Experience with deep learning frameworks: PyTorch (preferred) or TensorFlow
• Experience with Hugging Face Transformers or similar ecosystems
• Hands-on experience training or fine-tuning transformer models on GPUs (local or cloud-based)
• Previous experience using cloud platforms for model training or deployment (e.g., AWS, GCP, Azure, RunPod or similar GPU providers)
• Experience working with or fine-tuning open-weight LLM families (Gemma-3, Qwen-3.5, Llama 4, GPT-OSS, Mistral...)
• Hands-on experience with LoRA

Understanding of:

• Fine-tuning vs pretraining
• Overfitting and generalization
• Model evaluation
• Strong business awareness: ability to understand the context of the fine-tuning task and translate domain requirements into clear modeling objectives

What you bring

• MSc or PhD student in Computer Science, Machine Learning, AI, or related field
• Alternatively, 6 months of hands-on experience training and fine-tuning deep learning models
• Has worked on LLMs in research or industry
• Has fine-tuned at least one transformer model
• Comfortable working independently
• Interested in applied AI and real-world constraints (cost, latency, memory)

What You’ll Gain

• Real-world experience fine-tuning large models (30B–100B parameter class)
• Exposure to production constraints and deployment
• Opportunity to co-author technical writeups if applicable
• Strong applied portfolio project

What We Offer

• 100% Remote Work: Work from anywhere with flexibility and autonomy 
• Dynamic, High-Impact Projects: Work on cutting-edge ML and GenAI solutions across diverse industries
• International Clients: Collaborate with global organizations and solve real-world challenges at scale
• Urban Sports Club Membership: Supporting your physical and mental wellbeing
• Monthly Bolt Credits: For rides
• Company Events & Offsites: Regular team gatherings to connect, collaborate, and celebrate

About FirstPrinciples:
FirstPrinciples is a non-profit organization building an autonomous AI Physicist to understand the nature of reality: the underlying structure, governing principles, and fundamental laws of our universe. We're developing an intelligent system that can explore theoretical frameworks, reason across disciplines, and generate novel insights to tackle the deepest unsolved problems in physics. By combining AI, symbolic reasoning, and autonomous research capabilities, we're developing a platform that goes beyond analyzing existing knowledge to actively contribute to physics research. Our goal is to accelerate progress on the questions that have captivated humanity for centuries.

We operate as a global nonprofit organization, with a Canadian foundation, a US-based 501(c)(3).

Job Description:
We are looking for a Member of Technical Staff, Staff Physicist to help build an AI Physicist at the frontier of Quantum Information and AI. You will bring expertise in quantum information theory to help with training, evaluation methods, and set research direction for a rapidly evolving scientific system. This is a researcher role at the intersection of AI and physics: you will help invent new benchmarks, metrics, and evaluation methodologies for what it means to do high-quality research in Quantum Information with AI in the loop. You will work closely with research and engineering teams, and your contributions will flow straight into production model improvements and publishable outcomes.

Key Responsibilities:

Scientific Critique and Research Guidance:

• Review and critique model reasoning in quantum information and adjacent theory (eg; quantum error correction, cryptography, algorithms, etc).
• Identify subtle conceptual errors, missing assumptions, invalid proof steps, and “sounds right” failures.
• Provide clear corrections, alternative derivations, and minimal counterexamples that teach the system what good physics looks like.
• Translate domain judgment into actionable research recommendations for model behavior, reasoning style, and tool use.
• Create gold-standard demonstrations and reference solutions suitable for training and fine-tuning.
• Provide structured preferences and rankings over candidate model outputs to improve scientific reasoning quality using expert feedback loops.

Collaborators Program and Cross Functional Coordination:

• Work to help us build our Collaborators program, an external group of expert peers acting like a set of reviewers.
• Coordinate review cycles and incorporate collaborator feedback into training priorities, benchmark design, and evaluation criteria.
• Align external reviewer standards with internal research goals and engineering constraints, ensuring fast iteration while maintaining scientific defensibility.
• Communicate progress and open questions clearly across collaborators, research, and engineering.

Research Output and Publication:

• Help drive the system to produce outputs you would be proud to put your name on.
• Contribute to open-science artifacts where appropriate (benchmarks, datasets, technical reports, preprints).

Qualifications:

• Educational Background: PhD in Physics, Quantum Information, Theoretical CS, or closely related field, plus postdoctoral-level research maturity.
• Experience: Demonstrated ability to do research-grade reasoning in quantum information and to critique proofs, derivations, and scientific arguments with rigor. Experience contributing to evaluation methodology, benchmarking, or systematic error analysis in research settings is strongly valued.
• Technical Skills:   
  • Deep fluency in core quantum information topics (Quantum algorithms, gate quantum computer, annealing quantum computers, quantum error correction, foundation of quantum physics, quantum information theory, quantum field theory).
  • Strong mathematical foundations (linear algebra, probability, optimization, information-theoretic reasoning, differential equations, group theory, Lie Algebras, Hamiltonian and Lagrangian dynamics).
  • Scientific programming skills in Python plus standard research tooling (Git, LaTeX).
  • Working familiarity with modern ML training workflows and how expert feedback can be operationalized to improve model behavior.
• Collaboration & Communication:
  • Comfort working closely with engineers and researchers in a fast-moving, cross-functional environment.
  • Strong written communication, especially the ability to write precise critiques, crisp guidance, and benchmark specs that others can implement.
  • Ability to coordinate external reviewers and internal teams toward a shared standard of scientific quality.
• Mindset: Entrepreneurial & mission-driven, comfortable in a fast-growing, startup-style environment, and motivated by the ambition of tackling one of the greatest scientific challenges in history.

Bonus Skills:

• Experience at the intersection of quantum and machine learning.
• Familiarity with preference modeling, reward modeling, or building evaluation datasets for frontier models.
• Comfort with PyTorch and JAX or similar, and quantum tooling such as Qiskit, PennyLane, or Cirq.
• Publication record in high-impact physics journals.
  

Application Process:

• Interested candidates are invited to submit their resume, a cover letter detailing their qualifications and vision for the role, and references. Please include "Member of Technical Staff, Staff Physicist, Quantum Information and AI" in the cover letter.

Join us at FirstPrinciples and be a part of a transformative journey where science drives progress and unlocks the potential of humanity.

Job Description 

Buzz is revolutionizing the analytics and maintenance of power grid infrastructure through our advanced AI solutions. Our computer vision systems analyze critical infrastructure to enhance safety, reliability, and operational efficiency across the power grid network. We're seeking an experienced Machine Learning Engineer to help lead our computer vision initiatives. You'll drive the development of cutting-edge models for power grid analysis and provide a leadership anchor on a team of talented ML engineers.

Responsibilities

• Architect and lead end-to-end computer vision projects focused on: Equipment defect detection, Thermal anomaly identification, Vegetation encroachment monitoring, Surveillance of closed areas for human and animal intrusions
• Drive innovation by incorporating the latest advances in deep learning and generative AI to enhance model training, accuracy and reliability
• Develop production-grade Python libraries for the complete ML lifecycle
• Mentor team members and establish best practices for model development, evaluation, deployment, and monitoring
• Advocate for and uphold software quality standards within the ML team

Qualifications & Experience

• 7-10 years of industry experience in computer vision and machine learning
• Deep expertise in modern computer vision and deep neural networks including: Object detection, Semantic segmentation, Image classification, Similarity search, Vision language models
• Proven track record of deploying and maintaining ML models in production
• Expert proficiency in PyTorch, Lightning, OpenCV, and Scikit-Learn
• Proficiency in FastAPI and Pydantic
• Strong software engineering foundation including: Git version control, Test driven development (Pytest), CI/CD, ML devops, Python type hinting
• 2-3 years of proven leadership experience of technical teams

Desired Additional Experience

• Multi-modal computer vision 
• Custom object detection model development
• Generative models for data augmentation
• ML deployment on edge devices
• Extracting measurements from GIS and/or drone metadata enriched imagery
• Model quantization
• Systematic hyperparameter tuning

Additional information:

• This position does not include sponsorship for United States work authorization.

Multimodal Generative AI Researcher

Location: Remote 

About the Role

We’re looking for a Research Scientist with deep expertise in training and fine-tuning large Vision-Language and Language Models (VLMs / LLMs) for downstream multimodal tasks. You’ll help push the next frontier of models that reason across vision, language, and 3D, bridging research breakthroughs with scalable engineering.

What You’ll Do

• Design and fine-tune large-scale VLMs / LLMs — and hybrid architectures — for tasks such as visual reasoning, retrieval, 3D understanding, and embodied interaction.
• Build robust, efficient training and evaluation pipelines (data curation, distributed training, mixed precision, scalable fine-tuning).
• Conduct in-depth analysis of model performance: ablations, bias / robustness checks, and generalisation studies.
• Collaborate across research, engineering, and 3D / graphics teams to bring models from prototype to production.
• Publish impactful research and help establish best practices for multimodal model adaptation.

What You Bring

• PhD (or equivalent experience) in Machine Learning, Computer Vision, NLP, Robotics, or Computer Graphics.
• Proven track record in fine-tuning or training large-scale VLMs / LLMs for real-world downstream tasks.
• Strong engineering mindset — you can design, debug, and scale training systems end-to-end.
• Deep understanding of multimodal alignment and representation learning (vision–language fusion, CLIP-style pre-training, retrieval-augmented generation).
• Familiarity with recent trends, including video-language and long-context VLMs, spatio-temporal grounding, agentic multimodal reasoning, and Mixture-of-Experts (MoE) fine-tuning.
• Awareness of 3D-aware multimodal models — using NeRFs, Gaussian splatting, or differentiable renderers for grounded reasoning and 3D scene understanding.
• Hands-on experience with PyTorch / DeepSpeed / Ray and distributed or mixed-precision training.
• Excellent communication skills and a collaborative mindset.

Bonus / Preferred

• Experience integrating 3D and graphics pipelines into training workflows (e.g., mesh or point-cloud encoding, differentiable rendering, 3D VLMs).
• Research or implementation experience with vision-language-action models, world-model-style architectures, or multimodal agents that perceive and act.
• Familiarity with efficient adaptation methods — LoRA, adapters, QLoRA, parameter-efficient finetuning, and distillation for edge deployment.
• Knowledge of video and 4D generation trends, latent diffusion / rectified flow methods, or multimodal retrieval and reasoning pipelines.
• Background in GPU optimisation, quantisation, or model compression for real-time inference.
• Open-source or publication track record in top-tier ML / CV / NLP venues.

Equal Employment Opportunity:

We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.

The Role

• Contribute to cutting-edge research in scalable, distributed machine learning systems alongside experienced researchers and engineers. Explore new ways of building and verifying neural networks that operate across huge, decentralised, topologies of heterogenous devices.

Responsibilities

• Contribute to original research in deep learning with a focus on modular architectures, verifiability, continual learning, and scale
• Design and prototype novel neural network architectures for decentralized compute environments
• Contribute to joint publications and projects in collaboration with academic and industry researchers targeting top-tier AI venues such as NeurIPS, ICML, and ICLR

Competencies

Must Have 

• Currently enrolled in a PhD program (or, in exceptional cases, in a Master’s program) in Computer Science, Machine Learning, or a related field 
• Prior experience conducting original research, ideally with authorship or co-authorship on ML papers
• Strong understanding of deep learning fundamentals and experience working with in at least one major framework, e.g. PyTorch, JAX, or TensorFlow
• Self-directed, curious, and able to thrive in an environment with high autonomy
• Excellent written and verbal communication skills

Preferred

• Research experience in distributed systems, continual learning, or modular neural architectures
• A desire to contribute to open research and collaborate with the broader ML research community

Nice to Have

• Experience at the intersection of cryptography and machine learning

Please note: the benefits listed below apply to full-time employees only

JJanea Systems (USA) is a dynamic team of the best & brightest software engineering specialists and solutions innovators, from around the world. From kernel to cloud, we provide high-impact software development services to Fortune 500 companies.
We are urgently looking an exceptionally talented Senior Machine Learning Engineer to join our rapidly growing consulting team. In this role, you will have the opportunity to work at the cutting edge of the software industry and help work on the client's internal AI/ML practice, as well as utilize your LLM, Data Engineering, ML, and ML Ops skills while working with a team of highly skilled professionals in the AI/ML domain.

• To be considered for this position, you must have the following qualifications:

  • Bachelor's or Master’s degree in Computer Science or a related field
  • 4+ years of experience as a Software Engineer, Platform Engineer, ML Engineer, Data Scientist, AI Engineer, or Data Engineer
  • Flexibility in experience with different programming languages and willingness to adjust to project needs
  • Strong knowledge of Python
  • Knowledge of machine learning algorithms, data pre-processing methods, and ML frameworks (such as PyTorch, TensorFlow, Keras)
  • Experience with containers and Kubernetes in cloud environments (AWS, MS Azure, or GCP)
  • Familiarity with data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo)
  • Understanding of software testing, benchmarking, and continuous integration principles
  • Ability to translate business needs into technical requirements
  • Excellent communication and problem-solving skills, with the ability to break down complex challenges and develop innovative solutions
  • Being self-motivated and adaptable, with the ability to work effectively in fast-paced, dynamic environment
    
    
  Ideal candidates will also have:
  • Familiarity with agent frameworks (such as Langchain, Langgraph, IllamaIndex)
  • Familiarity with developing RAG systems
  • Experience with Natural Language Processing (NLP)
  • Familiarity with monitoring tools (such as DataDog or Langfuse)
  • Any associate cloud certification (AWS preferred)
  Responsibilities:
  • Design scalable data pipelines and infrastructure for enterprise ML systems
  • Implement ML models and systems into production
  • Collaborate with data scientists and software engineers
  • Deploy scalable tools and services for machine learning training and inference
  • Evaluate new technologies to improve ML system performance and reliability
  • Apply software engineering best practices, including CI/CD, to ML development
  • Facilitate the development and deployment of ML proof-of-concepts
  • Review, refactor, optimize, containerize, deploy, version, and monitor ML models
  • Implement monitoring and alerting solutions to ensure the reliability and performance of machine learning systems
  • Optimize and automate the machine learning deployment process to ensure efficiency and reproducibility
  • Collaborate with cross-functional teams to troubleshoot and resolve issues related to machine learning deployments
  • Stay updated with industry trends and apply knowledge to drive innovation
  • Promote industry best practices and enhance team expertise
    
    
  Why join Janea? Because world-class talent deserves world-class opportunities. What we offer:
  • Competitive compensation with benefits, paid vacation, and sick leave.
  • The opportunity to work with a globally diverse team of top engineering talent on the industry’s toughest engineering challenges.
  • Ultra-flexible working conditions – we provide a generous office equipment allowance so you can work from home, we can also provide you with a desk at an office/coworking facility near you, or use both. No business travel necessary.
  • An enjoyable, start-up work environment, with excellent opportunities for professional growth and development.
  • Flexible working hours – as a remote-first company, our focus has always been on getting the job done well, not when or where it gets done.
   

#LI-DNI