(junior) fullstack engineer (m/f/d) for a new product

ai engineer (m/f/d)

senior product engineer

ai & analytics engineer

ai enablement engineer

analytics engineer

ai research engineer

NuLegal

<div>Worum es geht

Wir bauen die Infrastruktur, die KI in der Rechtsbranche tats&auml;chlich belastbar macht, nicht als Demo, sondern in Produktion, mit messbarer juristischer Qualit&auml;t. Daf&uuml;r suchen wir Menschen, die tief in die Mathematik und Architektur moderner LLM-Systeme eintauchen wollen, nicht nur APIs verketten.

Wenn du dich in den letzten zw&ouml;lf Monaten gefragt hast, ob es irgendwo einen Job gibt, in dem du die ganze Tiefe moderner KI gleichzeitig brauchst: hier ist er.
<h2 class="jobSectionHeader">
Aufgaben</h2>

Dein Beitrag
<ul>
<li>Eine Multi-Provider-LLM-Council-Architektur, in der mehrere Modelle parallel arbeiten und &uuml;ber strittige juristische Fragen abstimmen, mit deterministischem Replay f&uuml;r reproduzierbare Evaluations.</li>
<li>Ein Eval-Framework von Grund auf: stratifizierte Gold-Standard-Datens&auml;tze, Inter-Rater-Reliability gegen menschliche Anw&auml;lte, Backtesting-Pipelines, Confidence-Kalibrierung.</li>
<li>Eine mathematisch belastbare Antwort auf die zentrale Forschungsfrage: Wie quantifiziert man &quot;juristische Belastbarkeit&quot;?</li>
<li>Eigene Forschungsbeitr&auml;ge, Konferenzpaper und Open-Source-Releases sind explizites Projektziel.</li>
<li>Eine produktionsreife Orchestrierungs-Infrastruktur, die t&auml;glich zehntausende Inferenz-Calls &uuml;ber mehrere Modellfamilien koordiniert, mit voller Observability und Fehlertoleranz.</li>
</ul>
Warum das spannend ist
<ul>
<li>Forschungstiefe wie in einer akademischen Gruppe, mit Produktions-Hebel wie in einem Top-Startup. Die meisten AI-Rollen sind API-Wrapper. Hier baust du die Schicht darunter.</li>
<li>Recht ist eine der gr&ouml;&szlig;ten noch unautomatisierten Branchen. Wer KI in einer traditionellen, hochregulierten Branche zum Laufen bringt, hat ein deutlich schwierigeres Problem als Web-Demos, und einen entsprechend gr&ouml;&szlig;eren Hebel.</li>
<li>Klare Deadlines, klare Deliverables. Ein 24-Monats-Plan mit hartem Meilenstein nach 12 Monaten und konkreten Forschungsoutputs.</li>
<li>Du wirst in 24 Monaten mehr &uuml;ber produktionsreife LLM-Systeme gelernt haben als in f&uuml;nf Jahren bei einem Konzern. Das versprechen wir dir.</li>
</ul><h2 class="jobSectionHeader">Qualifikation</h2>

Was du mitbringen solltest

Pflicht
<ul>
<li>Master oder Promotion in Informatik, Mathematik, Physik, Statistik oder einem verwandten quantitativen Fach.</li>
<li>Mehrj&auml;hrige Erfahrung in der Entwicklung produktionsreifer Software, kein reiner Forscher, aber auch kein reiner MLE.</li>
<li>Tiefes Verst&auml;ndnis von Wahrscheinlichkeitsrechnung, Statistik und Kalibrierung. Mathematische Beweisf&uuml;hrung schreckt dich nicht ab.</li>
<li>Tiefe Erfahrung in einem technisch anspruchsvollen Feld, in dem Mathematik, Skalierung und Reliability gleichzeitig z&auml;hlen.</li>
<li>Sicherer Umgang mit modernen LLMs und Agent-Frameworks.</li>
<li>Hohes Tempo und Teamorientierung. Du willst mit anderen ambitionierten Menschen in einem schlanken Team an einem gro&szlig;en Ziel arbeiten, nicht im Konzern-Rhythmus.</li>
</ul>
Stark gew&uuml;nscht
<ul>
<li>Wissenschaftliche Ver&ouml;ffentlichungen.</li>
<li>Open-Source-Beitr&auml;ge zu KI-Frameworks.</li>
<li>Erfahrung in einer regulierten Dom&auml;ne (z. B. Recht, Medizin, Finanzwesen).</li>
<li>Vertrautheit mit modernen Workflow-Systemen und Uncertainty Quantification.</li>
</ul>
(Das vollst&auml;ndige technische Anforderungsprofil ist auf Anfrage als separates Dokument verf&uuml;gbar.
<h2 class="jobSectionHeader">
Benefits</h2>

Was wir bieten
<ul>
<li>Flache Herachien und schnelle Entscheidungswege</li>
<li>Echter Impact von Tag 1</li>
<li>Eine Aufgabe, die du in dieser Tiefe nirgendwo sonst bekommst. AI im Recht ist Greenfield.</li>
<li>Wettbewerbsf&auml;higes Gehalt + Equity. Du baust mit auf, du wirst auch beteiligt.</li>
<li>Dediziertes Compute-Budget f&uuml;r deine Forschungs-Workloads.</li>
<li>Direkte Mitsprache an Architektur und Forschungsrichtung. Du sitzt am Tisch, an dem entschieden wird, vom ersten Tag an.</li>
<li>Plus 1 Monat pro Jahr Work from anywhere.</li>
</ul>
Bewerbung

Schick uns CV, Links zu relevantem Code oder Ver&ouml;ffentlichungen, und ein konkretes Beispiel eines technischen Problems, das du in den letzten 12 Monaten interessant gefunden hast, und warum.

Wir antworten auf jede ernsthafte Bewerbung innerhalb von zwei Wochen.</div>

AI Research Engineer

Control Risks

<div>As Senior AI Engineer you will own the AI and data layer of the platform. You will design and implement the knowledge graph architecture, build agentic pipelines that enrich entity data through AI web research, and develop the graph interrogation layer that translates natural language compliance questions into structured graph queries.
You will work closely with the CTO and a small AI team including a data engineer and a junior AI engineer. You will have significant technical autonomy and direct influence over how the product evolves.
Please submit yoru CV in English. 
Requirements 
 
What You&rsquo;ll Work On
Knowledge graph architecture and implementation<ul><li>Own and evolve the platform&rsquo;s knowledge graph data model &mdash; entities (Company, Person, Community, Episodic), relationships, and temporal attributes</li><li>Implement and extend custom entity and relationship extraction pipelines using LMs, with structured output validation and confidence scoring</li><li>Use Graphiti (Zep AI&rsquo;s temporal knowledge graph framework) as the memory layer, managing episode ingestion, entity resolution, and graph updates</li><li>Design and enforce graph schema standards, ensuring consistency across data sources and ingestion pipelines</li><li>Build evaluation frameworks to measure extraction quality, entity disambiguation accuracy, and graph coverage</li></ul>
Agentic AI pipelines<ul><li>Design and implement multi-step agentic workflows that orchestrate LLM calls, web search, database lookups, and graph writes</li><li>Integrate AI web search (Tavily, Perplexity, or similar) as a tool within agentic pipelines for real-time entity enrichment</li><li>Build retrieval-augmented generation (RAG) pipelines over the knowledge graph, translating compliance queries into Cypher and natural language answers</li><li>Implement the graph interrogation layer &mdash; a conversational interface for compliance analysts to query the knowledge graph without writing Cypher</li><li>Manage LLM API integrations (Anthropic Claude, Gemini) including prompt engineering, structured outputs, and cost/latency optimisation</li></ul>
Data pipelines and integrations<ul><li>Build and maintain Python-based data ingestion pipelines reading from Databricks, connecting to external APIs (sanctions lists, corporate registries, Polixis, Vantage), and writing to Neo4j</li><li>Implement NER (named entity recognition) and entity disambiguation logic for extracting structured facts from unstructured compliance documents</li><li>Develop topic modelling pipelines to classify extracted facts as knowledge graph relationship attributes</li><li>Integrate with the .NET backend via well-defined HTTP contracts, ensuring the AI layer is independently deployable and testable</li></ul>
Research and technical leadership<ul><li>Stay current with the fast-moving agentic AI and knowledge graph literature &mdash; evaluate new frameworks, models, and techniques for production applicability</li><li>Contribute to architectural decisions with evidence: benchmarks, prototypes, and documented trade-offs</li><li>Maintain Claude Code context files (CLAUDE.md, SKILLS.md) for the AI codebase, enabling AI-augmented development across the team</li><li>Write clear technical documentation in Notion covering design decisions, data models, and operational runbooks.</li></ul>
What We&rsquo;re Looking For
Must-have<ul><li>3+ years of professional experience in AI/ML engineering, with at least 1 year working on knowledge graphs or graph-based data systems in production</li><li>Hands-on experience with Neo4j &mdash; data modelling, Cypher query writing, schema design, and performance tuning</li><li>Deep familiarity with LLM APIs (OpenAI, Anthropic, or Gemini) &mdash; prompt engineering, structured outputs, function/tool calling, and cost management</li><li>Understanding the trade-offs between LLMs and smaller fine-tuned models (Hugging Face Transformers, sentence-transformers) &mdash; knowing when a purpose-built model outperforms a prompted general one on cost, latency, and accuracy</li><li>Experience building agentic pipelines &mdash; multi-step LLM workflows with tool use, memory, and state management</li><li>Python proficiency &mdash; you write clean, testable Python and understand async patterns for I/O-bound AI workloads</li><li>Experience with RAG architectures &mdash; vector search, hybrid retrieval, and integrating retrieval into LLM-powered applications</li><li>Ability to read and apply AI research papers &mdash; you can take a paper on narrative extraction or entity disambiguation and turn it into a working prototype</li><li>Strong English communication skills &mdash; written and verbal</li></ul>
Strong advantage<ul><li>Direct experience or knowledge with Graphiti (Zep AI) or other temporal knowledge graph frameworks</li><li>Familiarity with entity resolution and disambiguation techniques at scale</li><li>Experience with NER pipelines, topic modelling, or information extraction from compliance or financial documents</li><li>Knowledge of the third-party risk, sanctions screening, or KYC/AML domain</li><li>Experience with Databricks, Delta Lake, or similar data lakehouse platforms</li><li>Exposure to GCP (Vertex AI, Cloud Run, Pub/Sub, Cloud Storage)</li><li>Experience designing evaluation frameworks for LLM-generated structured outputs</li><li>Prior work in a RegTech, FinTech, or compliance-adjacent environment</li></ul>
What Good Looks Like in This Role
We are direct about what we expect. The person who thrives here will:<ul><li>Treat the knowledge graph data model as the product&rsquo;s most important artefact &mdash; they think carefully about schema decisions and document them</li><li>Read the literature. When evaluating a new approach to entity disambiguation or graph memory, they check what&rsquo;s been published, not just what&rsquo;s on GitHub</li><li>Write evaluation harnesses before scaling pipelines. They know that a pipeline that produces plausible-looking output is not the same as one that produces correct output</li><li>Communicate blockers and trade-offs early. Technical uncertainty is expected &mdash; silence is not</li></ul></div>

Senior AI Engineer

ALMETRA

<div>We&rsquo;re looking for a Research Engineer to push the limits of vision-language models for real-world video understanding. You&rsquo;ll work on applied, state-of-the-art multimodal models and turn them into production pipelines used by customers.<h3 class="jobSectionHeader">
Your role</h3><ul><li>
Design and adapt vision-language and video models for scene understanding, temporal reasoning and activity / action recognition</li><li>
Build and maintain large-scale training and evaluation pipelines on GPU clusters</li><li>
Curate and augment video-text and action datasets, including synthetic labels and retrieval-based augmentation</li><li>
Develop robust benchmarks for video QA, instruction following and temporal understanding, and use them to drive iterative model improvements</li><li>
Cut and refactor model architectures for efficiency and deployability (compression, pruning, distillation)</li><li>
Deliver production-ready inference pipelines to product and customer teams, working closely with CV, platform and robotics engineers</li></ul><h3 class="jobSectionHeader">
You bring</h3><ul><li>
Completed PhD (or equivalent research track record) in computer vision, machine learning, robotics or a related field</li><li>
Strong background in video-centric deep learning: scene understanding, temporal / activity / action recognition, or video generation</li><li>
Experience training and adapting large vision or VLM models (e.g. InternVL, Qwen-VL, DeepSeek-VL, similar stacks)</li><li>
Proven work with multi-GPU training (PyTorch, distributed, mixed precision) and large-scale datasets</li><li>
Solid engineering habits: clean Python, reproducible experiments, reliable data and training pipelines</li><li>
Track record of moving research into usable systems (demos, internal tools, or productised features) in fast-moving teams</li></ul><h3 class="jobSectionHeader">
Nice to have</h3><ul><li>
Publications at top-tier venues (CVPR, ICCV, ECCV, NeurIPS, ICLR, etc.) on video, multimodal learning or scene understanding</li><li>
Experience with 3D/4D scene representations, action generation or embodied / sense-plan-act style projects</li><li>
Inference optimisation: quantisation, TensorRT, model distillation, or deployment on constrained hardware</li><li>
Prior experience in a startup or applied research lab environment</li></ul>
What we offer
Employee Share Options Program for all permanent employees*
An increasing benefits list: currently includes Urban Sports club and quarterly team retreats.
Be on the forefront in defining what artificial intelligence means in manufacturing
Gain hands-on experience in working in an AI-first software company
Supportive and inclusive culture that values diversity and promotes the advancement of underrepresented groups within the company
Collaborate with a diverse (currently more than 10 nationalities) and talented team, working on cutting-edge projects with real-world impact
Network with professionals and leaders in the field, opening doors to potential future career opportunities
We have a very flat hierarchy, open 360&deg; feedback, and flexible working hours
Ethics: We are committed to developing ethical AI software 
 
Don't meet all the requirements?
Almetra is committed to creating a workplace that is diverse, fair, and inclusive. We encourage candidates from all backgrounds, even if they do not meet every qualification, to submit their application. We firmly believe that having a team with diverse perspectives only strengthens our company and drives innovation. Our commitment also extends to providing an accessible environment for everyone, including those with disabilities. Please let us know if you require any accommodations during the application process or while working with us, and we will do our best to support you.<ul><li> 
Only full-time, permanent roles are eligible for stock options. Part-time roles, contract roles, work-student, internships and freelance roles are not eligible for stock options.</li></ul></div>

VLM Research Engineer (m/f/d)

Code Compass 🧭

<div>
 
 🤖 Machine Learning Engineer &ndash; Computer Vision &amp; Generative AI &ndash; 100%
 
 
 
 
 
 📍 Berlin, Germany | 💰 &euro;50,000 - &euro;70,000 + Benefits | 🌍 Hybrid Working
 
 
 
 
 
 Our client is a technology-driven organisation developing intelligent software solutions that leverage artificial intelligence, automation, and advanced data processing to solve complex business challenges at scale.
 
 
 
 
 
 Operating at the intersection of machine learning, software engineering, and product innovation, they are investing heavily in next-generation AI capabilities and expanding their engineering team to support the continued evolution of their platform and services.
 
 
 
 
 
 This role is ideal for a Machine Learning Engineer who enjoys taking cutting-edge AI solutions from research to production and wants to work on technically challenging projects with tangible real-world impact.
 
 
 
 
 
 ⚙️ Tech Stack:
 
 
 Python, PyTorch, TensorFlow, Computer Vision, Generative AI, Diffusion Models, GANs, Image Processing, Machine Learning, Docker, Cloud Platforms, Production ML Systems, Automation, AI Workflows
 
 
 
 
 
 🚀 About the Role:
 
 
 
 
 <ul>
 <li>
 As a Machine Learning Engineer, you will:
 </li>
 <li>
 Develop, train, and optimise machine learning models focused on computer vision and generative AI applications
 </li>
 <li>
 Build and deploy scalable ML solutions into production environments
 </li>
 <li>
 Design automation solutions that reduce manual effort and improve operational efficiency
 </li>
 <li>
 Monitor model performance, identify improvement opportunities, and enhance reliability and accuracy
 </li>
 <li>
 Collaborate closely with engineering, product, and operational teams to deliver practical AI-driven solutions
 </li>
 <li>
 Research emerging technologies and rapidly evaluate their suitability for business applications
 </li>
 <li>
 Contribute to the evolution of production-grade machine learning infrastructure and workflows
 </li>
 </ul>
 
 
 
 
 ➕ Nice to Have:
 
 
 
 
 <ul>
 <li>
 Several years of hands-on experience within machine learning and computer vision
 </li>
 <li>
 Strong Python development skills with experience using frameworks such as PyTorch or TensorFlow
 </li>
 <li>
 Practical experience working with generative AI technologies including diffusion models and GANs
 </li>
 <li>
 Understanding of ML deployment, scalability, performance optimisation, and production environments
 </li>
 <li>
 Experience with Docker and cloud-based infrastructure
 </li>
 <li>
 Knowledge of creative AI tools and visual content workflows
 </li>
 <li>
 Experience working with image-based products, digital content platforms, or related industries
 </li>
 <li>
 Strong analytical thinking combined with a pragmatic, solution-oriented mindset
 </li>
 <li>
 Ability to communicate effectively in an international and cross-functional environment
 </li>
 </ul>
 
 
 
 
 💪 Benefits:
 
 
 
 
 <ul>
 <li>
 Opportunity to work on cutting-edge AI and machine learning technologies
 </li>
 <li>
 Highly collaborative and innovative working environment
 </li>
 <li>
 Flexible working arrangements with hybrid options
 </li>
 <li>
 Exposure to challenging technical problems with real-world impact
 </li>
 <li>
 Professional development support, training, and continuous learning opportunities
 </li>
 <li>
 Modern working environment with strong team culture and regular social activities
 </li>
 </ul>
 
 
 
 
 
 ✉️ Due to the volume of applications, we may not be able to respond to everyone individually. However, we genuinely appreciate every application and will contact shortlisted candidates directly.
 
 
</div>

Machine Learning Engineer

BIT Capital

<div>
 
 
 Your role &amp; How we work
 
 
 
 
 
 
 We are hiring an AI Engineer (m/f/d) to holistically develop BIT Capital's LLM and agentic Artifical Intelligence capabilities: from the data science that turns data into investment signals, to the engineering that powers our financial intelligence agents. A strong initial focus is the build-out of &ldquo;Aion&rdquo; - our agentic research platform - that has first-class access to our data, models, and tools and is already in mainstream production, used by the investment team every day. Beyond, your mandate covers everything LLM- and agent-related across BIT, from research products for the investment team to internal tools in Sales, Marketing, Operations, and client-facing experiences.
 
 
 
 
 
 You work autonomously, from first scoping to shipping, and you self-select the use cases and projects where you can have the most impact. You report to our Carlos Bielsa (Chief AI Officer &amp; Managing Partner) and work side by side with Vlad Gheorghe, our second AI Engineer, acting as our internal expert as the frontier of AI moves. Our stack is Python, SQL, AWS, and Databricks, with models and agentic frameworks from the leading AI labs (Anthropic Managed Agents, Codex).
 
 
 
 
 
 Your priorities in this role will thus include:
 
 <ul>
 <li>
 
 Investment signal pipelines.
 
 Design and ship LLM-based extraction pipelines (prompts, retrieval, model selection, evaluations) that turn text and data into investment scores, alerts, signals, and insights reliable enough to drive decisions.
 </li>
 <li>
 
 Agentic systems behind &ldquo;Aion&rdquo;.
 
 Build and evolve the agentic systems that power Aion, along with the research skills and Python sidecars that agents execute.
 </li>
 <li>
 
 Data and tool integrations.
 
 Build the integrations that give agents performant, safe access to BIT's data and capabilities.
 </li>
 <li>
 
 Evaluations and model selection.
 
 Build automated and human-in-the-loop evaluations, regression tests, and monitoring that track accuracy, hallucination rates, latency, cost, and model uncertainty. Benchmark and select the right model per task, including open-source options.
 </li>
 <li>
 
 LLMOps at scale.
 
 Establish the LLMOps practices that keep these systems reliable at scale: versioning of prompts, configs, and models; CI/CD; error handling; fallbacks; and cost control.
 </li>
 <li>
 
 Frontier into production.
 
 Stay at the frontier of LLM and agentic AI techniques and bring new ideas into production rather than leaving them as experiments.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 The experience you bring
 
 
 <ul>
 <li>
 You have a solid data science and engineering background, with at least 2 years building software, data systems, or ML in production.
 </li>
 <li>
 You adopt the mindset of a product-minded software engineer. You have shipped your own apps and agents and can point us to a public project, repo, or product.
 </li>
 <li>
 You are fluent with the current LLM and agentic frontier, with hands-on experience using leading agentic systems (Claude Code, Anthropic Managed Agents, Codex, open-source agentic loops).
 </li>
 <li>
 You have strong Python and SQL skills with clean engineering practices, plus cloud infrastructure experience (AWS strongly preferred).
 </li>
 <li>
 You bring production discipline for probabilistic systems: evaluations, regression tests, monitoring, reproducibility, and cost awareness. A scientific approach to turning LLMs into signals and scores (ground-truth datasets, train/test/iterate) is a plus; a data science or ML background helps but is not required.
 </li>
 <li>
 You have a genuine interest in investing, or at minimum a strong drive to learn about our domain quickly and deeply, paired with sharp general intelligence and clear systems thinking.
 </li>
 </ul>
 
 
 
 
 
 2 reasons why you should not apply
 
 
 <ul>
 <li>
 You mainly use AI tools for your own productivity and have not yet taken systems into production. This role is about building and shipping AI that the whole firm relies on, so prior production experience matters here.
 </li>
 <li>
 You are looking for a finished, stable AI stack to operate and maintain. Our systems evolve quickly as the frontier moves, so you will spend much of your time building and reshaping them rather than keeping a fixed setup running.
 </li>
 </ul>
 
 
 
 
 
 Your mindset
 
 
 <ul>
 <li>
 You're an "AI Native": you use agentic AI as core infrastructure in how you work, not as a novelty.
 </li>
 <li>
 You're a "Builder": you ship working systems and improve them continuously rather than leaving ideas as experiments.
 </li>
 <li>
 You blend data science rigor with engineering pragmatism, with care for correctness, reliability, and cost.
 </li>
 <li>
 You're passionate about AI, emerging technology, and investing, and you pair technical depth with strong product judgment.
 </li>
 <li>
 You manage for outcomes, own problems end-to-end, and communicate clearly on progress, tradeoffs, and risk.
 </li>
 </ul>
 
 
 
 
 
 What we offer
 
 
 <ul>
 <li>
 Work at the frontier of applied AI in asset management, with petabytes of data and the most cutting-edge tools.
 </li>
 <li>
 Real technical direction and ownership from day one, as the internal expert while the frontier moves.
 </li>
 <li>
 Contribute to a unique success story in European asset management, surrounded by the most knowledgeable experts in the industry.
 </li>
 <li>
 A motivated, highly international team (15 nationalities) that enjoys getting together in person in Berlin and strongly supports each other.
 </li>
 <li>
 Flat hierarchies and direct interaction with our management team.
 </li>
 <li>
 Competitive compensation, relocation, and visa support if needed.
 </li>
 </ul>
 
 
 
</div>

AI Engineer (m/f/d)

Peec AI

<div>
 What you&rsquo;ll do
 
 
 <ul>
 <li>
 Train, test, and ship models that power Peec AI&rsquo;s recommendations - helping customers boost their visibility in AI search
 </li>
 <li>
 Develop algorithms that extract actionable insights from AI search behavior, creating data-driven recommendations that help brands increase their AI Search visibility
 </li>
 <li>
 Own the full model lifecycle from experimentation to production deployment, working closely with engineering to integrate ML solutions into our systems
 </li>
 <li>
 Design and implement data pipelines to ingest, process, and analyze large volumes of data
 
 
 </li>
 </ul>
 What we&rsquo;re looking for
 
 
 <ul>
 <li>
 Proven backend development skills in Python with experience building APIs, data pipelines, or ML infrastructure, and familiarity with tools like FastAPI, Docker, and cloud platforms (preferably GCP)
 </li>
 <li>
 Deep curiosity about how LLMs work, with the ability to reverse-engineer AI search behavior and translate patterns into actionable product features
 </li>
 <li>
 Track record of taking projects from research to production in a fast-moving startup environment, with strong problem-solving skills and comfort working with ambiguous, evolving problems
 </li>
 <li>
 Excellent communication skills with the ability to explain complex technical concepts to customers and stakeholders
 
 
 </li>
 </ul>
 Our Data Science Stack
 
 
 <ul>
 <li>
 Languages: Python, SQL
 </li>
 <li>
 Libraries: Pandas, NumPy, HuggingFace, PyTorch, TensorFlow, ONNX
 </li>
 <li>
 Backend: GCP, Cloud Functions, Firestore, Postgres, AlloyDB, BigQuery
 </li>
 <li>
 AI Models: OpenAI, Claude, Perplexity, Gemini, Llama, etc.
 
 
 </li>
 </ul>
 
 Bonus Points
 
 
 
 <ul>
 <li>
 Contributions to open-source projects
 </li>
 <li>
 Deployed side/hobby projects that we can check out
 </li>
 <li>
 Presented research papers at top ML or AI conferences
 </li>
 <li>
 Having started a company before or worked at a high-growth startup
 </li>
 <li>
 Fluency in Typescript
 
 
 </li>
 </ul>
 What we offer
 
 
 <ul>
 <li>
 Exciting and challenging work with real impact and ownership at one of Europe&rsquo;s fastest-growing Series A startups
 </li>
 <li>
 Regular team events and off-sites
 </li>
 <li>
 Aggressive equity compensation package
 </li>
 <li>
 Paid Dinner &amp; Uber home when working late
 </li>
 <li>
 The most beautiful office space and work environment in Berlin
 </li>
 </ul>
</div>

Data Scientist

Mercor

<div>
 
 About The Job
 
 
 
 
 Mercor
 
 connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include
 
 Benchmark
 
 ,
 
 General Catalyst
 
 ,
 
 Peter Thiel
 
 ,
 
 Adam D'Angelo
 
 ,
 
 Larry Summers
 
 , and
 
 Jack Dorsey
 
 .
 
 
 
 Position:
 
 ML Engineer (Coding Agent Experience)
 
 
 
 Type:
 
 
 Contract
 
 
 
 
 Compensation:
 
 
 $85/hour
 
 
 
 
 Location:
 
 
 Remote
 
 
 
 
 Role Responsibilities
 
 
 
 <ul>
 <li>
 Use frontier AI coding agents to complete and evaluate complex machine learning and AI engineering tasks.
 </li>
 <li>
 Review model-generated implementations involving model training, inference systems, MLOps, and LLM applications.
 </li>
 <li>
 Identify bugs, edge cases, performance issues, and failure modes in AI models.
 </li>
 <li>
 Compare outputs from multiple frontier models and assess their strengths and weaknesses.
 </li>
 <li>
 Apply professional engineering judgment to realistic ML engineering scenarios.
 
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 
 Must-Have
 
 
 
 <ul>
 <li>
 2+ years of professional machine learning engineering experience.
 </li>
 <li>
 Experience building production ML systems, model deployment infrastructure, LLM applications, or AI-powered products.
 </li>
 <li>
 Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools.
 </li>
 <li>
 Ability to evaluate model-generated machine learning implementations and technical tradeoffs.
 
 
 
 </li>
 </ul>
 
 Preferred
 
 
 
 <ul>
 <li>
 Experience deploying ML systems to production.
 
 
 
 </li>
 </ul>
 
 Compensation &amp; Legal
 
 
 
 <ul>
 <li>
 $400 per accepted task
 </li>
 <li>
 Compensation is tied to accepted work.
 
 
 
 </li>
 </ul>
 
 Application Process (Takes 20&ndash;30 mins to complete)
 
 
 
 <ul>
 <li>
 Upload resume
 </li>
 <li>
 AI interview based on your resume
 </li>
 <li>
 Submit form
 
 
 
 </li>
 </ul>
 
 Resources &amp; Support
 
 
 
 <ul>
 <li>
 For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
 </li>
 <li>
 For any help or support, reach out to: support@mercor.com
 
 
 
 </li>
 </ul>
 
 PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.
 
</div>

AI/ML Engineer - Fully Remote | Upto $85/hr

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