Deep Learning Engineer (Computer Vision)

About OneSec

OneSec is a Swiss-American startup that has opened an R&D center in Istanbul.

We are looking for talented Machine Learning engineers to join our Team.

OneSec is an AI & Robotics company, working on the next generation of Autonomous delivery drones.

You can find more info on our website: OneSec.com

About this job

OneSec is building a 3D-Foundation model, in one of the largest machine learning projects in the robotics industry. Using a dataset of more than 150 million images, and modern machine learning techniques, we are building a frontier-level foundation model for autonomous drone navigation.

Your job will consist of 2 main elements:

-Machine learning: Developing model architectures, tuning hyperparameters, working on GPU training infrastructure, etc.

-Dataset generation: Using open source AI models to generate pseudo labels for internet images, generating synthetic images using geometrical transformations, filtering datasets for valuable training data etc.

Requirements

-Strong expertise in deep learning

-Experience with computer vision and CNNs (resnets etc)

-Experience with Pytorch (and ideally CUDA, OpenCV, etc)

-General tech skills: Computer Infrastructure, image processing, etc.

Benefits

-Attractive salary & stock option package

-Working on frontier level AI

-Work in an exceptionally talented team

-State of the art development tools & training infrastructure

About OneSec:

OneSec is an AI-first robotics company, making food delivery 3 times faster and 10 times cheaper, by developing a novel type of delivery drone. Visit our website for more info: www.OneSec.com

 

Job Description

As Deep Learning and Computer vision engineer, you will develop and train the AI models that turn the drones camera images into a usable 3D representation of the environment. You will work with large custom datasets, to build a deep neural network we call the 3D Foundation model. You will work on CNN architectures, hyperparameter tuning, training infrastructure, and computer vision scripts from data augmentation and generation.

 

Requirements

• Strong expertise in deep learning
• Experience with computer vision and CNNs (resnets etc)
• Experience with PyTorch & CUDA
• Good generalist tech skills: cloud infrastructure, image processing etc.

 

Why Join OneSec?

• Work in an exceptionally talented team
• Attractive salary & stock option package
• Work on cutting-edge autonomous drone technology
• State-of-the-art development tools & equipment

Apilex is an AI-native legal operating system that manages lawyers' entire legal workflows end to end, powered by AI systems purpose-built for each jurisdiction's legal framework. We bring legal research, drafting of pleadings and contracts, document analysis, and case preparation together in a single experience. We currently operate in France, Turkey, and Germany, and we aim to extend the growth we've achieved in our current markets to Brazil, Spain, and Italy.

We achieved this growth entirely bootstrapped, without raising any external investment, making us one of the fastest go-to-market success stories among Legal AI startups in the world. Since our founding, we've scaled our team to 120+ people, backed by a proprietary database of millions of court decisions and legal documents that turns days of legal research into seconds.

What sets us apart?
We don't use off-the-shelf AI. We build domain-specific, closed-circuit AI models with multi-layered verification systems that eliminate hallucinations and deliver answers backed by real citations and legal reasoning. Every feature we ship changes how lawyers work.

We are looking for a
Senior Technical Business Analyst.
This role sits at the core intersection of Product, AI, and Software Engineering. You will not just translate requirements—you will architect how complex legal and business workflows are transformed into highly scalable, deterministic, and advanced AI-agent behaviors.

Key Responsibilities

• Architect AI & Agent-Driven Workflows:
  Lead the design and analysis of complex, multi-step AI reasoning flows. Define orchestration logic, decision trees, and interaction protocols for multi-agent systems.
• Bridge Business Complexity & AI Engineering:
  Translate intricate legal processes and operational needs into robust technical specifications, production-ready system flows, and comprehensive PRDs/User Stories.
• Own the System Documentation:
  Create sophisticated system blueprints, technical flow diagrams, and edge-case mappings that serve as the single source of truth for AI and backend engineers.
• Establish AI Evaluation & Quality Guardrails:
  Design advanced testing methodologies and evaluation frameworks specifically for GenAI systems. Define benchmarks for accuracy, hallucination mitigation, context window optimization, and prompt robustness.
• Drive Continuous AI Optimization:
  Proactively monitor system outputs, identify behavioral gaps or logical bottlenecks in AI responses, and engineer high-impact improvement actions (e.g., tweaking prompt strategies, adjusting agent routing, refining data inputs).
• Cross-Functional Leadership:
  Act as a strategic technical partner to Product Managers, AI Engineers, and UX Designers, ensuring rapid iteration cycles and a flawless transition from concept to production.

What We’re Looking For

• 5+ years of experience
  as a Technical Business Analyst, Product Analyst, or Systems Analyst, with a significant track record in complex tech environments.
• At least 2 years of hands-on experience
  working directly with LLM-powered products, prompt engineering, or agentic workflows.
• Deep understanding of GenAI capabilities & limitations:
  Proven knowledge of tokenization, embeddings, context engineering, RAG (Retrieval-Augmented Generation) architectures, and mitigation strategies for hallucinations/bias.
• Advanced Systems Thinking:
  Exceptional ability to deconstruct ambiguous, unstructured legal or business problems into rigorous, deterministic algorithmic flows.
• Strong Technical Literacy:
  Proficiency in API logic, data modeling, JSON structures, and basic architectural patterns.
• Agile Leadership:
  Mastery of product management and documentation tools (Jira, Linear, Confluence, Miro/Lucidchart, Figma).
• Data-Driven & Detail-Oriented:
  Ability to define clear success metrics, write bulletproof acceptance criteria, and lead complex QA/validation processes for non-deterministic AI features.

**Why join us?

**

Whatever your role, your work here will be visible and meaningful. We're a team that has grown without outside investment and achieved one of the fastest go-to-market successes in the space and we're looking for ambitious people who want to build the future of legal technology on a global stage. 

This is Apilex. AI for Legal. Join us
.

Apilex is an AI-native legal operating system that manages lawyers' entire legal workflows end to end, powered by AI systems purpose-built for each jurisdiction's legal framework. We bring legal research, drafting of pleadings and contracts, document analysis, and case preparation together in a single experience. We currently operate in France, Turkey, and Germany, and we aim to extend the growth we've achieved in our current markets to Brazil, Spain, and Italy.

We achieved this growth entirely bootstrapped, without raising any external investment, making us one of the fastest go-to-market success stories among Legal AI startups in the world. Since our founding, we've scaled our team to 120+ people, backed by a proprietary database of millions of court decisions and legal documents that turns days of legal research into seconds.

What sets us apart?
We don't use off-the-shelf AI. We build domain-specific, closed-circuit AI models with multi-layered verification systems that eliminate hallucinations and deliver answers backed by real citations and legal reasoning. Every feature we ship changes how lawyers work.

As a Senior AI Engineer, you will design, build, and operate AI systems used across real legal workflows. You will take ownership of technically challenging projects, contribute to architecture decisions, and work closely with researchers, engineers, and legal domain experts to turn new methods into reliable production systems.

Key Responsibilities

• Design, build, and improve AI systems for complex legal reasoning and long-horizon task execution.
• Develop agentic systems that can plan, use tools, manage state, recover from errors, and complete end-to-end legal workflows reliably.
• Build graph-based multi-agent orchestration systems that represent workflows, dependencies, state transitions, and coordination between specialized agents.
• Design and implement graph engineering solutions across GraphRAG, personalized knowledge bases, and multi-agent orchestration.
• Build GraphRAG systems that connect structured and unstructured legal knowledge to improve retrieval, reasoning, and source-grounded outputs.
• Develop personalized knowledge bases that incorporate user, organization, matter, and jurisdiction-specific context while respecting data isolation and access controls.
• Work on graph schemas, ontologies, entity and relationship extraction, entity resolution, graph storage, and retrieval pipelines for legal knowledge.
• Optimize agent harnesses, including tool interfaces, context management, memory, orchestration, and execution environments.
• Contribute to model post-training for advanced legal reasoning, including supervised fine-tuning, preference optimization, and reinforcement learning approaches where appropriate.
• Develop reward models, verifiers, and automated verification systems for evaluating legal reasoning, citations, intermediate steps, and final outputs.
• Build scalable pipelines for producing, filtering, and validating high-quality synthetic and human-generated training data.
• Create evaluation methodologies, domain-expert benchmarks, and regression suites that measure capability, reliability, and behavior across jurisdictions.
• Improve experimentation and observability systems so that model and agent changes are measurable, reproducible, and safe to deploy.
• Make sound engineering decisions across model selection, inference infrastructure, latency, cost, reliability, and performance.
• Participate in architecture reviews, share knowledge with the team, and support other engineers through technical collaboration and code reviews.

What We’re Looking For

• 5+ years of engineering experience building and operating AI or machine learning systems in production.
• Strong Python engineering skills and experience designing reliable, scalable systems.
• Hands-on experience building agentic systems that use tools, maintain state, and execute multi-step tasks.
• Hands-on experience building production-grade RAG systems.
• Experience working with graph-based systems, such as knowledge graphs, GraphRAG pipelines, graph databases, or graph-based agent workflows.
• Strong understanding of retrieval systems, including chunking, embeddings, reranking, structured retrieval, entity resolution, and graph traversal.
• Strong understanding of evaluation design, including benchmark construction, error analysis, model comparisons, and regression testing.
• Experience building or working with verifiers, reward models, judge systems, or other automated methods for assessing model outputs.
• Familiarity with training data pipelines, synthetic data generation, data filtering, and quality assurance.
• Practical understanding of modern language model infrastructure, including inference, serving, experimentation, and observability.
• Ability to work effectively across open-ended research problems and production engineering constraints.
• Strong technical judgment and a practical approach to solving problems where established best practices may not yet exist.

Nice to Have

• Experience with long-horizon agents, reinforcement learning for language models, or inference-time scaling.
• Experience with model post-training methods such as supervised fine-tuning, preference optimization, reinforcement learning, or distillation.
• Experience designing knowledge graph schemas, ontologies, entity resolution systems, or graph retrieval pipelines.
• Experience with graph databases such as Neo4j, Amazon Neptune, Memgraph, or similar technologies.
• Experience building personalized or organization-specific knowledge systems with permission-aware retrieval and secure data isolation.
• Experience evaluating reasoning systems in high-stakes or expert domains.
• Experience working with legal documents, citation systems, or domain-expert evaluation.
• Research contributions, open-source work, or publications related to language models, agents, evaluation, knowledge graphs, interpretability, or AI reliability.

Why join us?

Whatever your role, your work here will be visible and meaningful. We're a team that has grown without outside investment and achieved one of the fastest go-to-market successes in the space and we're looking for ambitious people who want to build the future of legal technology on a global stage.

This is Apilex. AI for Legal. Join us.