we are looking for a Machine Learning Engineer.
What You'll Do:
- Design and ship the ML backbone of Gini AI Workers - routing, tool selection, reasoning, memory, evaluation.
- Build evaluation and feedback loops - offline evals, online A/B, regression harnesses, human-in-the-loop labeling pipelines.
- Optimize cost and latency across the agent stack: prompt engineering, model routing (frontier ↔ small ↔ fine-tuned), caching, speculative decoding, distillation.
- Fine-tune and/or RAG-tune models for vertical enterprise tasks (invoice extraction, PO matching, ticket triage, forecasting).
- Own the ML infra - training pipelines, experiment tracking, model registry, deployment, monitoring, drift detection.
- Partner with backend + product to turn research into shipped features on a weekly cadence.

Join our companys AI research group, a cross-functional team of ML engineers, researchers and security experts building the next generation of AI-powered security capabilities. Our mission is to leverage large language models to understand code, configuration, and human language at scale, and to turn this understanding into security AI capabilities which will drive our company AI future security solutions.
We foster a hands-on, research-driven culture where youll work with large-scale data, modern ML infrastructure, and a global product footprint that impacts over 100,000 organizations worldwide.
Key Responsibilities
Your Impact & Responsibilities
As a Senior ML Research Engineer, you will be responsible for the end-to-end lifecycle of large language models: from data definition and curation, through training and evaluation, to providing robust models that can be consumed by product and platform teams.
Own training and fine-tuning of LLMs / seq2seq models: Design and execute training pipelines for transformer-based models (encoder-decoder, decoder-only, retrievalaugmented, etc.), and fine-tune open-source LLMs on our company-specific data (security content, logs, incidents, customer interactions).
Apply advanced LLM training techniques such as instruction tuning, preference / contrastive learning, LoRA / PEFT, continual pre-training, and domain adaptation where appropriate.
Work deeply with data: define data strategies with product, research and domain experts; build and maintain data pipelines for collecting, cleaning, de-duplicating and labeling large-scale text, code and semi-structured data; and design synthetic data generation and augmentation pipelines.
Build robust evaluation and experimentation frameworks: define offline metrics for LLM quality (task-specific accuracy, calibration, hallucination rate, safety, latency and cost); implement automated evaluation suites (benchmarks, regression tests, redteaming scenarios); and track model performance over time.
Scale training and inference: use distributed training frameworks (e.g. DeepSpeed, FSDP, tensor/pipeline parallelism) to efficiently train models on multi-GPU / multi-node clusters, and optimize inference performance and cost with techniques such as quantization, distillation and caching.
Collaborate closely with security researchers and data engineers to turn domain knowledge and threat intelligence into high-value training and evaluation data, and to expose your models through well-defined interfaces to downstream product and platform teams.

We're backed by tier-1 global VCs, led by second-time founders, and already deployed with organizations operating at serious scale. AI is not a feature here - it's the system.
We're hiring a Senior AI Engineer to design, fine-tune, and operate AI agents and large-scale models in production. This role exists because off-the-shelf models aren't enough for the problems we're solving.
If you enjoy pushing models until they break - and then fixing them - keep reading.
What you'll do:
Design and operate AI agents that reason, act, and collaborate with humans
Fine-tune and adapt large language models for:
Behavior analysis
Reasoning over long, messy timelines
High-precision enterprise workflows
Build agent orchestration systems (tool use, memory, planning, feedback loops)
Run large-scale inference and training pipelines in production
Work on model evaluation, drift detection, and continuous improvement
Optimize for latency, cost, and reliability at real enterprise scale
Partner closely with DevOps, security, and backend engineers - no research silos
Ship models that are auditable, explainable, and safe in sensitive environments

We are looking for a Senior AI Engineer to design and build the intelligence layer.
You will own the agentic workflows, LLM integrations, and reasning pipelines that allow our AI agents to autonomously analyze markets, make decisions, and drive eCom growth at scale.
What you'll do:
Design & Build Agentic Workflows: Architect multi-agent pipelines - including planning, memory, tool use, and decision loops - that power autonomous media buying and growth operations.
Own LLM Integration: Select, prompt-engineer, fine-tune, and evaluate LLMs to produce reliable, high-quality outputs across diverse business tasks.
Build RAG Systems: Develop retrieval-augmented generation pipelines with vector search and context management to ground agent reasoning in real business data.
Drive Evaluation & Reliability: Define evals, build testing frameworks, and continuously improve agent output quality, consistency, and safety in production.
Collaborate Across the Stack: Work closely with backend engineers to integrate AI capabilities into core product APIs, ensuring low-latency, production-grade deployment.

Were looking for a highly motivated AI Developer to help design, build, and deploy intelligent agentic systems across our product ecosystem. In this role, you'll work at the intersection of machine learning, backend systems, and modern frontend technologies to deliver AI-first features that feel magical to users.

This is a hands-on, cross-functional role ideal for engineers who love building full-fledged features-from data pipelines and LLM orchestration to intuitive UI experiences-with a strong product mindset.

Responsibilities:
AI Agent Design & Integration
Design and implement autonomous or semi-autonomous agents using LLMs (e.g., OpenAI, Anthropic, open-source models).
Work with prompt engineering, RAG pipelines, and tool/plugin integrations to enable agents to interact with internal and external systems.
Build scalable agent runtimes and orchestration layers (e.g., LangChain, Semantic Kernel, ReAct-based agents).
Fullstack Product Development
Own full-stack features end-to-end: from backend APIs and data models to React-based frontend interfaces.
Integrate AI/agent capabilities into customer-facing products with clean UX and measurable performance.
Collaborate closely with design, product, and data teams to bring ideas from concept to production.
Systems & Infrastructure
Build and maintain backend services and pipelines that support AI agents, including vector search, embeddings, function calling, and observability.
Optimize inference flows for performance and cost, potentially using streaming, caching, or local model inference.
Ensure systems are secure, reliable, and compliant with InfoSec standards.
Experimentation & Continuous Improvement
Rapidly prototype and iterate on new AI capabilities and user experiences.
Analyze performance and usage metrics to drive product and model improvements.
Stay up to date with the evolving AI toolchain and emerging agent architectures.

A Senior AI/ML Engineer role on our Data team, sitting at the intersection of AI engineering, data science, and data platform. You'll build agentic, LLM-based systems that turn vast amounts of unstructured information into high-quality structured signals - powering our products and surfacing legal risk - owning each solution across the full lifecycle, from exploration to production. You'll work closely with data engineers, data scientists, product managers, and legal experts to turn ambiguous problems into scalable AI capabilities. This is a hands-on role for someone with strong engineering judgment, deep data/ML expertise, and a focus on reliability beyond the prototype stage.

What You'll Do

Design, build, deploy, and operate production AI systems: agentic, LLM-based extraction workflows and batch/online inference pipelines and owning them from exploration through deployment, monitoring, and iteration.
Define quality metrics and build evaluation datasets, testing processes, and feedback loops for ML and LLM systems; monitor accuracy, latency, cost, and data quality, and investigate and fix failures.
Explore large, complex datasets to surface useful features and patterns, and translate findings into production-grade implementations.
Partner with data engineers on ingestion, transformation, orchestration, and storage to deliver dependable data products.
Use AI-assisted coding tools (e.g., Claude) effectively while maintaining strong technical judgment and ownership of your systems.
Help shape engineering standards for production AI: testing, observability, reproducibility, data lineage, versioning, and safe deployment.

We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.

Were looking for a Senior MLOps Engineer to be a core driver in how our product empowers security teams. You will be expected to deeply understand customer needs and translate them directly into product features that deliver real value. You'll own key parts of our frontend stack, drive key architectural decisions, and turn complex security data into clear, actionable business insights.

As we scale our AIDR product and expand deeper into model-driven security intelligence, we are looking for a Senior MLOps Engineer to own the infrastructure, tooling, and operational foundations that power our NLP and LLM training, evaluation, and deployment workflows.

You will architect and operate the systems that enable us to train, fine-tune, deploy, and monitor models at scale making our ML reliable, fast, cost-efficient, and production-ready.

This is a high-visibility, high-impact role where you will partner closely with DevOps, Backend, Data, and Product to establish world-class ML infrastructure from the ground up.

What Youll Do
Build & Scale ML Pipelines
Design, build, and maintain pipelines for training, fine-tuning, evaluating, and deploying NLP and LLM models across GPU and CPU environments.
Establish LLM-Focused CI/CD
Implement automated CI/CD workflows for ML models, including benchmarking, testing, performance gating, and production deployment.
Optimize Runtime & Inference
Select and optimize serving frameworks for low-latency, high-throughput inference, ensuring reliability and scalability.
Own ML Infrastructure
Manage training environments, experiment tracking, model registries, artifact versioning, and distributed training systems.
Operational Excellence
Monitor and optimize production models for performance, cost efficiency, availability, and observability.

We are looking for an experienced Applied AI/ML Scientist with expertise in building agentic systems and autonomous agents to join one of our R&D. You will be at the core of transforming our supply chain solutions into a fully agentic platform, designing and building agents that autonomously generate analytical pipelines, orchestrate multi-step reasoning, and resolve complex logistics challenges for our customers. You will combine strong machine learning and deep learning expertise with the ability to architect and implement production-grade agentic systems, working closely with engineering, product, and domain experts to push the boundaries of what autonomous AI can do in supply chain.
Responsibilities:
Design and build autonomous agentic systems that generate, configure, and execute analytical pipelines to solve supply chain challenges end-to-end
Architect multi-agent workflows with planning, tool use, memory, and feedback loops, enabling agents to reason, adapt, and improve over time
Develop and integrate ML and deep learning models (e.g., predictive models, anomaly detection, demand forecasting) as core capabilities within agentic pipelines
Research and apply state-of-the-art techniques in agentic AI, LLM orchestration, and multi-agent systems to production use cases
Translate complex logistics and supply chain challenges into agent-based problem formulations, collaborating closely with product and domain experts
Define and implement rigorous evaluation frameworks for agent performance: correctness, reliability, robustness, and edge-case handling
Collaborate with software engineers to productize agentic solutions - including testing, monitoring, versioning, and iterative improvement
Contribute to team practices: reproducible code, experiment tracking, documentation, and knowledge sharing

We are looking for a strong Backend Software Engineer to bridge the gap between our Machine Learning research team and our enterprise production systems. You will act as the technical backbone for our ML Scientists - by advising, designing and implementing the production facing features. If you are a backend expert who wants to solve complex system architecture challenges and dive into the world of ML platforms & Agentic LLM pipelines, this is the role for you - An exciting role collaborating with ML science team, data/infra team and DevOps to drive real customer impact.



As a ML Engineer, you will:



Lead ML delivery: transforming research output (code, models, ideas) into robust, scalable, low-latency microservices in production

Help architect e2e solutions to real customer pains ranging from ingestion, integration, ETLs, DB design up to low-latency services

Design, build, and maintain automated workflows for ML models, including auto-trains, benchmarking, testing, performance gating, and production deployment.

Tackle complex backend challenges: optimizing API response times, managing database connectivity and concurrency at scale, balancing accuracys drive for complex questions with the business needs of fast responsiveness by making hard technical trade-offs between customer gains and business costs.

Design and optimize data pipelines and ETL processes, connecting our Snowflake data warehouse to our training environments.

Work within our existing ML infrastructure (Kubeflow, MLflow, KServe) to ensure smooth model lifecycles and performance monitoring.

Collaborate closely with ML Scientists, guiding them on software engineering best practices without slowing down their research.

Monitor and optimize production models for performance, cost efficiency, availability, and observability.