Highlights:

Role: Senior ML-Engineer, Finance

Location: Serbia, Remote

Language: Strong English required (C1)

About Us

Fundraise Up is a modern fundraising platform built to make donating to nonprofits as fast and convenient as possible. We continuously innovate to reduce page load times, boost conversion rates, and support a wide range of payment methods. Each month, people around the world contribute tens of millions of dollars through our platform.

The world’s leading nonprofit organizations trust Fundraise Up. UNICEF, the most prominent UN charity, uses our platform for 100% of its online fundraising. So does the American Heart Association, the Alzheimer’s Association, and many others. We’re proud to maintain a 4.9 out of 5 rating on leading review platforms.

We serve the enterprise segment, with a primary client base in the US, Canada, UK, and Australia.

The Team

Our product development team is currently at 150+ and growing. Team members are located across Spain, Serbia, Poland, Portugal, Turkey, Cyprus, Georgia and Armenia. We primarily communicate in Russian.

We’re a tight-knit, high-impact team where every task matters. It’s a dynamic, collaborative environment where smart, curious engineers support one another, share knowledge, and strive for excellence. We encourage open dialogue and host bi-weekly engineering meetups to explore technical topics and showcase team insights.

About the Role

We're looking for an ML Engineer with 5+ years of production experience to own a high-impact client intelligence initiative. Following a successful proof-of-concept with an external consultant, we are bringing this project fully in-house. The ultimate goal is to generate a comprehensive, enriched list of all potential clients globally — understanding their product requirements, industry verticals, and overall revenue potential — and deploy a scoring model that feeds directly into our sales pipeline.

This is an end-to-end ownership role. You will build from the ground up: data collection, enrichment, modeling, and production deployment. The project is co-managed by company executives and has a high strategic value.

What You’ll Do

• Build a market intelligence data-base via collecting different types of data (scraping, enrichment), fixing data pipeline and creating an ML model for scoring and analysis of the raw data.
• Design and operate scrapers to extract key signals from nonprofit websites, including products used, payment tools, and industry vertical indicators.
• Develop critical filters such as an "Is this website for fundraising?" binary classifier, alongside other features that distinguish high-potential prospects.
• Source and integrate financial data from international nonprofit registries, as well as third-party signals from SimilarWeb and Facebook.
• Store and structure the enriched dataset in our internal database, making it accessible and useful across the broader team for research and analysis.
• Work closely with the sales team to understand their qualification criteria. Analyze disqualified accounts in Salesforce to identify common exclusion patterns and refine scoring accordingly.
• Deploy the scoring model and own the process of integrating outputs into Salesforce in a clean, maintainable way.
• Build a scraper to monitor existing clients' websites, tracking whether Fundraise Up tools are correctly implemented across their properties.

Challenges You'll Navigate

• At the scale of ~1 million domains, expect domain duplicates, inconsistent data, and significant noise. You'll need to develop robust, cost-efficient filtering pipelines.
• A single model won't cover everything — you'll likely build several targeted sub-models tailored to specific verticals and geographies as the project matures.
• All of this needs to be accomplished without incurring high infrastructure or data costs. Pragmatic, scrappy solutions are valued here.

Requirements

• 5+ years of ML/DS experience solving real product problems
• Strong expertise in ML and mathematical statistics: solid knowledge of classical algorithms (especially gradient boosting) and understanding of modern NLP/LLM approaches
• Proven experience with large-scale web scraping and data pipeline construction
• Metrics-driven mindset: ability to connect ML metrics (ROC-AUC, F1, RMSE) with business metrics (conversion rate, LTV)
• Strong engineering culture: confident in Python with a product-oriented approach; we value clean code, knowledge of design patterns, and solid engineering practices
• Advanced SQL; ability to independently build complex datasets in ClickHouse and work with MongoDB
• MLOps understanding: hands-on experience with experiment tracking and production workflows (Docker, Git, CI/CD)
• Autonomy: ability to break down ambiguous problems, choose the right tech stack, and deliver to production

Our Tech Stack

Core: Python (uv, ruff), FastAPI, Pydantic, Docker

Models: CatBoost, Uplift Modeling (CausalML), OpenAI (RAG, Prompt-Engineering)

Data: ClickHouse, MongoDB, pandas, Polars, Redis

MLOps: MLflow, Airflow

Monitoring: Grafana, Sentry

Infra: linux server admin, distributed computation

Bonus points

• Curiosity and a hypothesis-driven mindset
• Ability to communicate complex analytical concepts to non-technical audiences
• Detail-oriented with a strong sense of ownership
• Comfort working in fast-paced, data-rich environments

Why work with us

• A strong, collaborative product team that owns what it builds
• Clear product vision and access to real customer feedback from global nonprofit leaders
• Flat structure: no politics, just great work with great people
• Transparent company culture-we share how we’re growing, where revenue comes from, and what’s next
• Long-term focus: we offer equity options and value sustained, meaningful contribution

Benefits

• Private medical insurance for the employee and their family
• 20 paid vacation days per year
• 15 paid public holidays per year
• 5 company-paid sick leave days
• English learning courses
• Relevant professional education
• Gym or swimming pool
• Home Office Setup Assistance: the company offers assistance with purchasing furniture (office chair, office desk, monitor) and other items to create a comfortable workspace
• Co-working
• Remote working

_{**Please note: All official correspondence from Fundraise Up will exclusively originate from the @fundraiseup.com domain. Exercise caution and ensure the authenticity of emails claiming to be from our company.}

Highlights:

Role: Senior ML-Engineer

Location: Serbia, Remote

Language: Russian-speaking team; Strong English required (B2)

About Us

Fundraise Up is a modern fundraising platform built to make donating to nonprofits as fast and convenient as possible. We continuously innovate to reduce page load times, boost conversion rates, and support a wide range of payment methods. Each month, people around the world contribute tens of millions of dollars through our platform.

The world’s leading nonprofit organizations trust Fundraise Up. UNICEF, the most prominent UN charity, uses our platform for 100% of its online fundraising. So does the American Heart Association, the Alzheimer’s Association, and many others. We’re proud to maintain a 4.9 out of 5 rating on leading review platforms.

We serve the enterprise segment, with a primary client base in the US, Canada, UK, and Australia.

The Team

Our product development team is currently at 150+ and growing. Team members are located across Spain, Serbia, Poland, Portugal, Turkey, Cyprus, Georgia and Armenia. We primarily communicate in Russian.

We’re a tight-knit, high-impact team where every task matters. It’s a dynamic, collaborative environment where smart, curious engineers support one another, share knowledge, and strive for excellence. We encourage open dialogue and host bi-weekly engineering meetups to explore technical topics and showcase team insights.

About the Role

We're looking for an ML Engineer with 5+ years of production experience to strengthen our ML team. We operate as an internal service and centre of excellence for 10+ product teams at Fundraise Up. This means you won't be tied to a single feature: one day you might be optimising donation amounts and upsell offers, and the next you could be building a smart assistant for the admin panel or working on transaction classification.

We actively use not only classical ML, but also RL, and we're expanding our LLM-based solutions — prompt engineering and pipeline design with LLM APIs (OpenAI and equivalents). That's why we're looking for someone with a broad mindset who isn't afraid to experiment and can choose the most effective approach for each task.

The project's main audience and business team are based in the US. Although the product development team is Russian-speaking, you may occasionally need to write in English.

What You’ll Do

• Develop and deploy ML solutions across different business areas using tabular data (uplift models, recommender systems). We are also actively developing projects that will involve e-commerce mechanics.
• Select the most appropriate ML/LLM approaches or propose alternative solutions.
• Build end-to-end ML solutions: data preparation, training, API development, and monitoring.
• Design prompts and LLM API-based pipelines for specific product tasks: classification, content generation, and response quality evaluation.

Requirements

• 5+ years of ML/DS experience solving real product problems
• Strong expertise in ML and mathematical statistics: solid knowledge of classical algorithms (especially gradient boosting); NLP knowledge is a plus
• Metrics-driven mindset: ability to connect ML metrics (ROC-AUC, F1, RMSE) with business metrics (CR, LTV)
• Strong engineering culture: confident in Python with a product-oriented approach to development; we value clean code, knowledge of design patterns, and solid engineering practices
• Data skills: advanced SQL; ability to independently and efficiently build complex datasets in ClickHouse and work with MongoDB
• MLOps understanding: hands-on experience with experiment tracking tools and understanding of production workflows (Docker, Git, CI/CD)
• Autonomy: ability to break down problems, choose the right tech stack (or justify a non-ML solution), and deliver to production

Our Tech Stack

Core: Python (uv, ruff), FastAPI, Pydantic, Docker

Models: CatBoost, Uplift Modeling (CausalML), OpenAI (RAG, Prompt-Engineering)

Data: ClickHouse, MongoDB, pandas, Polars, Redis

MLOps: MLflow, Airflow

Monitoring: Grafana, Sentry

Why work with us

• A strong, collaborative product team that owns what it builds
• Clear product vision and access to real customer feedback from global nonprofit leaders
• Flat structure: no politics, just great work with great people
• Transparent company culture-we share how we’re growing, where revenue comes from, and what’s next
• Long-term focus: we offer equity options and value sustained, meaningful contribution

Benefits

• Private medical insurance for the employee and their family
• 20 paid vacation days per year
• 15 paid public holidays per year
• 5 company-paid sick leave days
• English learning courses
• Relevant professional education
• Gym or swimming pool
• Home Office Setup Assistance: the company offers assistance with purchasing furniture (office chair, office desk, monitor) and other items to create a comfortable workspace
• Co-working
• Remote working

_{**Please note: All official correspondence from Fundraise Up will exclusively originate from the @fundraiseup.com domain. Exercise caution and ensure the authenticity of emails claiming to be from our company.}

At JetBrains, code is our passion. Ever since we started back in 2000, we have been striving to make the world’s most robust and effective developer tools. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

We are now building services and agentic tools that provide AI coding agents and end users with deeper context about codebases. Our code retrieval service already delivers meaningful improvements in agent speed and task performance, and we aim to push this further – extracting richer insights than snippets alone at the scale of several hundred thousand repositories. 

We are looking for an AI Engineer who can design and implement agentic tools from scratch, bring them to the end users, and make coding with agents smarter, faster, and more reliable.

In this role, you will:

• Translate product needs into agentic tools and services and ship them to production.
• Scale prototypes to large numbers of users.
• Improve and test our semantic search service.
• Build evaluation pipelines for code retrieval and coding task performance.

We’ll be happy to have you on our team if you have:

• At least five years of hands-on NLP experience.
• Experience in information retrieval systems.
• Strong experience in LLM prompt and tool engineering, as well as with AI agents.
• Solid programming skills in Python and/or Kotlin (and the willingness to learn the language you’re unfamiliar with).
• Experience building and operating production-grade software systems.

We’d be especially thrilled if you have:

• Experience developing in Kotlin or Java.
• Previously worked with code intelligence (e.g. AST, symbol graphs).

#LI-DNI

Software engineers and AI agents alike suffer from the same problem: finding that one person or place that will answer their tough, specific question. Many solutions promise to solve this with similarity search in vector databases. Unfortunately, finding the answer is often a puzzle with pieces to be collected across a myriad of contradictory sources and cannot be solved without surgical search and careful reasoning.

Spectrum collects data from an organization's code, docs, and issues, and organizes knowledge in a unified ontology that AI agents can efficiently search through and reason over. We aim to revolutionize the semantic layer space for software-building organizations and move beyond specs that fall out of sync with code, introducing a living spec – one that's extracted from the whole system and used to keep it aligned. Spectrum is meant to be the single source of truth for all product and architectural knowledge.

A resident of JetBrains' startup incubator, Spectrum enjoys startup speed and autonomy, and is backed by 25 years of developer tooling expertise. We are looking for a Senior ML Researcher to develop the core methods that make Spectrum possible – novel approaches to temporal ontology extraction, contradiction detection, and semantic alignment across heterogeneous software artifacts. You will help define and execute the research agenda, while also collaborating with JetBrains Research and external academic advisors.

Your responsibilities will include:

• Developing methods for LLM-based ontology construction from code, documentation, and issue trackers, including contradiction detection and reasoning over the resulting knowledge graph.
• Creating datasets, metrics, and benchmarks that drive measurable improvements across the system.
• Prototyping and validating research ideas, then collaborating with ML and software engineers to bring them to production.
• Collaborating with JetBrains Research and external academic advisors to shape our research agenda.
• Publishing research findings at top venues and representing Spectrum in the research community.

We expect you to have:

• A PhD (or equivalent research experience) in NLP, knowledge graphs, ontology learning, information extraction, or a closely related field.
• A strong publication record in at least one of the following areas: knowledge graph construction, ontology learning, information extraction, or NLP.
• Experience applying LLMs to structured knowledge extraction tasks.
• Experience designing and running rigorous experiments, including hypothesis formulation, ablation studies, and statistical evaluation.
• Strong Python and PyTorch skills, with the ability to implement research ideas from scratch.
• Excellent communication skills, with the ability to explain complex technical concepts to diverse audiences.
• Proficiency in English, both written and verbal.

Our ideal candidate would also have:

• Experience with ontology engineering, semantic web technologies (OWL, RDF, SPARQL), or ontology alignment.
• A background in code analysis, developer tools, or software engineering research.
• Experience with knowledge graph embedding methods or graph neural networks.
• Experience in early-stage startups – you enjoy the zero-to-one phase.
• Actively contributed to relevant open-source projects.

What we offer:

• A competitive salary and JetBrains benefits.
• A generous runway and corporate resources with startup autonomy.
• Full support for publishing ML research and attending industry conferences.
• Collaboration with JetBrains Research and external academic advisors in the knowledge graph and ontology learning community.
• A motivated team that moves fast, with short feedback loops, rapid prototyping, and lots of ownership.
• A rare applied research role where your methods ship in a real product used by software teams.

Why join JetBrains? 

• Strong base salary. We offer competitive pay that reflects your skills and experience.
• Flexible work location. Enjoy the freedom to work from home or from the office.
• Remote work. Spend up to 30 days per year working remotely from abroad.
• Extra time off. More days to relax, recharge, and do the things you love.
• Medical insurance allowance. Enjoy peace of mind for you and your family
• Learning and development opportunities. Access to conferences, courses, and language classes.
• Language classes. Pick up the local language or sharpen your English skills.
• Fuel your day. Enjoy a hot meal or receive a lunch allowance on workdays.
• Mental health support. To help you feel your best, we provide easy access to professional mental health services.
• Sports benefit. Enjoy an on-site gym or sports club stipend.
• Internal events. Join company-wide celebrations and team gatherings.

*Some benefits may vary depending on location.

#LI-DNI

About the role:

ABBYY Vantage is our flagship Document AI platform – a cloud-native, microservices-based product that combines purpose-built AI, OCR, NLP, and direct LLM integration to help enterprise customers extract, classify, and act on data from any document. Vantage 3.0, our latest release, introduces native Azure OpenAI integration, enterprise-grade compliance and redaction, a real-time analytics dashboard, and a growing library of pre-configured AI skills. Over 10,000 customers trust ABBYY – including Fortune 500 companies like DHL, Johnson & Johnson, FDA, PwC, KeyBank, Spotify, and H&R Block – to automate their most complex document workflows.

We are looking for a Staff Software Engineer to serve as a senior technical leader on the Vantage platform. You will drive the design and delivery of complex, cross-cutting capabilities – spanning document AI pipelines, LLM integrations, compliance infrastructure, analytics, and platform extensibility. Beyond your own code, you will act as a technical multiplier: influencing architectural decisions, setting quality standards, and developing the engineers around you. This is a role for someone who leads through depth, judgment, and impact.

Job Responsibilities:

• Technical Leadership: Drive the technical design and delivery of significant Vantage platform capabilities, from architecture and API contracts through deployment, monitoring, and iteration.
• Quality & Standards: Define and uphold engineering standards for the Vantage codebase – covering code quality, service reliability, performance, security, and compliance – and drive their adoption across teams.
• Strategic Collaboration: Act as a senior technical voice in cross-functional planning with product management, ML engineering, cloud architecture, and engineering leadership; translate business priorities into actionable technical roadmaps.
• Platform Improvement: Lead initiatives to improve Vantage platform health at scale: service decomposition, observability, CI/CD maturity, and developer productivity.
• Operational Excellence: Own high-severity incident response for Vantage cloud services; drive post-mortems and systemic reliability improvements.
• Engineering Mentorship: Mentor Senior and Principal Engineers on the Vantage team; build a culture of technical rigor, shared ownership, and continuous learning.
• Technical Innovation: Evaluate and champion adoption of new technologies – from GenAI model integrations to cloud-native tooling – that strengthen ABBYY's Document AI differentiation.

Job Requirements:

• 10–13 years of professional software engineering experience with deep expertise in C# .NET and C++, willing to work with both languages
• Proven experience designing, implementing, and operating CI/CD pipelines for building, testing, and deploying workflows using GitHub Actions and/or Azure DevOps Pipelines
• Strong command of microservices architecture, distributed system design, and cloud-native patterns at enterprise scale
• Demonstrated ability to mentor Senior and Mid-level Engineers and partner with Engineering Managers on technical direction
• Experience leading technical design reviews and influencing architectural decisions across team and product boundaries

Desired Qualifications

• Experience in cloud, preferably using Microsoft Azure (AKS, Storage, Key Vaults, Azure SQL or equivalent)
• Track record of delivering production-grade systems that meet enterprise compliance, security, and reliability standards
• Hands-on experience designing and operating workflow-driven systems (orchestration, retries, timeouts, and state management)
• Experience integrating Machine Learning, Neural Networks or LLMs into enterprise applications and workflows
• Comfort operating across a globally distributed engineering organization spanning multiple regions and cultures

About the role:

ABBYY Vantage is our flagship Document AI platform – a cloud-native, microservices-based product that combines purpose-built AI, OCR, NLP, and direct LLM integration to help enterprise customers extract, classify, and act on data from any document. Vantage 3.0, our latest release, introduces native Azure OpenAI integration, enterprise-grade compliance and redaction, a real-time analytics dashboard, and a growing library of pre-configured AI skills. Over 10,000 customers trust ABBYY – including Fortune 500 companies like DHL, Johnson & Johnson, FDA, PwC, KeyBank, Spotify, and H&R Block – to automate their most complex document workflows.

We are looking for a Senior Software Engineer to join the Vantage engineering team. You will be a hands-on contributor building and evolving the services that power Vantage's document AI capabilities – from OCR pipelines and skill training workflows to API integrations and analytics infrastructure. You bring strong engineering fundamentals, a quality-first mindset, and a drive to deliver reliable, scalable software in a cloud-native environment.

Job Responsibilities:

• End-to-End Development: Design, develop, test, and deploy features and services within the Vantage platform, contributing to areas such as document processing pipelines, AI skill management, cloud infrastructure, and API layers.
• Code Quality: Write clean, maintainable, well-tested code; actively participate in code reviews and champion engineering best practices across your team.
• Cross-Functional Collaboration: Work closely with product managers, UX designers, ML engineers, and fellow software engineers to translate Vantage feature requirements into well-architected solutions.
• Continuous Improvement: Identify and surface improvements to existing Vantage services, tooling, and delivery workflows; contribute to reducing technical debt and increasing engineering velocity.
• System Reliability: Monitor the health and performance of Vantage cloud services; investigate and resolve production incidents in a timely manner.
• Technical Growth: Stay current with advances in Document AI, cloud-native patterns, and GenAI integration approaches that could enhance the Vantage platform.

Job Requirements:

• 5–9 years of professional software engineering experience with deep expertise in C# .NET and C++, willing to work with both languages
• Proven experience designing, implementing, and operating CI/CD pipelines for building, testing, and deploying workflows using GitHub Actions and/or Azure DevOps Pipelines
• Good understanding of microservices architecture, distributed system design, and cloud-native patterns at enterprise scale
• Demonstrated track record of delivering high-quality, testable code
• Strong analytical and debugging skills with the ability to investigate complex issues across distributed systems

Desired Qualifications

• Experience in cloud, preferably using Microsoft Azure (AKS, Storage, Key Vaults, Azure SQL or equivalent)
• Hands-on experience designing and operating workflow-driven systems (orchestration, retries, timeouts, and state management)
• Experience integrating Machine Learning, Neural Networks or LLMs into enterprise applications and workflows
• Familiarity with OCR, NLP and computer vision technologies
• Comfort operating across a globally distributed engineering organization spanning multiple regions and cultures

Mission

As a AI Translation Engineer, you will be at the forefront of advancing our platform’s translation capabilities. Leveraging your expertise in machine learning including applied research, fine tuning, benchmarking, and inference optimization, you will deliver high-performance solutions that power critical features for our growing client base.

Requirements

• Evaluation engineering: Translates product features into measurable user use cases; selects a small set of reliable offline metrics; builds benchmarks and runs slice analysis across languages and domains; designs rubrics and reference-less MT evaluation for ambiguous cases; defines online metrics and guardrails, runs A/B tests and staged rollouts, and validates that offline gains translate into production impact while spotting metric loopholes and regressions
  
  
• Data fluency: Owns end-to-end dataset pipelines: multi-source ingestion → cleaning/validation → scalable multilingual datasets, with versioning and auditability
  
  
• Classical NLP foundations: Understands MT/search text normalization and core matching approaches (exact/fuzzy, typo tolerance, multi-word + overlap handling, tries/Aho–Corasick/inverted index basics), and applies them to improve preprocessing and retrieval
  
  
• LLM operations: Operates external LLM providers end-to-end: designs prompts for structured generation/evaluation; builds automated filters/validators; generates synthetic data at scale using self-consistency and multi-provider “consilium”; selects and routes across providers based on quality–latency–cost; and uses self-hosted models when helpful for quality analysis/judging and provider fine-tuning data prep
  
  
• Practical production engineering: Can write lightweight services/jobs when needed and ship them with structured logs, monitoring/alerts, reliability basics (retries, idempotency, fallbacks), and scalability considerations; collaborates effectively with Dev/Platform

Key Responsibilities

• Develop and improve benchmarks for translation problems
• Work with NER, word alignment, automatic translation quality scoring system, and other problems
• Set up infrastructure for self-hosted solutions

Our technologies

• ML/Backend: Python, C#, PyTorch, Transformers
• DB: MongoDB, PostgreSQL, Elasticsearch
• Messaging: Apache Kafka
• Cloud: Amazon AWS
• Monitoring: ELK, Prometheus, Grafana

Mission:

The Head of AI will play a key role in driving the success of Smartcat’s mission to become the end-to-end Language AI platform for global enterprises. In this role, you will shape Smartcat's AI strategy, unlocking the potential of AI to create new, innovative use cases or reinvent existing user experiences, delivering measurable value to our enterprise clients in multilingual content generation, translation, and analysis.

Outcomes:

• Create, enhance, and maintain Smartcat’s AI strategy, solidifying our market position as the leading Language AI platform for enterprises
• Lead with a vision on the future of AI, anticipating where the industry is heading and how to create strong tech IP and defensibility in the areas that are aligned with the company strategy
• There are three high-impact areas to focus on over the next 12 months:
• Language AI Quality: Smartcat delivers distinctly higher quality in AI-generated translations and content for enterprise customers, thanks to continuous feedback from their employees, which helps improve AI output. Your goal will be to introduce a holistic approach to measuring language quality across various use cases for translation and multilingual content generation. You will aim to achieve consistent improvement in these metrics quarter over quarter for our enterprise customers, significantly reducing the required editing effort on their end. (We are already making progress in this area and have achieved measurable results, but there is still enormous potential for innovation and breakthroughs.)
  You will also lead the development of a systematic technological approach to AI quality customization, using use case-specific AI agents integrated with our platform and data layers.
• Redesigning Product Experiences: Working with the UI/UX and other product teams, you will redesign every existing product experience and use case with an AI-first approach, focusing on improvements in quality, turnaround time, and creating innovative user experiences.
• AI-Driven Customer Lifecycle: Smartcat has strong inbound and PLG/PLS motions, with thousands of new verified logos signing up on the platform every quarter. You will lead the creation of an AI-driven customer lifecycle product within the Smartcat platform, leveraging real-time data on each user’s intent and in-product actions to deliver best-in-class, customized onboarding and support experiences. This product will also tailor the overall UX and guide users through their next steps within the platform.
• You will continue building and managing a team of top AI/ML engineers, fostering a culture of innovation, urgency, and customer-centricity.

Requirements:

• 10+ years of experience as an Engineering and Product leader with at least 2+ years leading AI/ML unit for a tech company
• A deep understanding of the current state of ML and AI, along with a natural passion and curiosity to stay ahead of the rapidly evolving AI landscape and drive innovation within it.Experience creating and driving a strategic AI roadmap with the ability to inspire and align a team
• Strategic mindset with a proactive approach to identifying opportunities and challenges
• Ability to effectively present and communicate at the executive and board level
• Readiness to work in a highly intense startup environment requiring extreme focus, a strong sense of ownership, and persistence to breakthrough
• Openness to constructive feedback and the opinions and ideas of others
• Strong preference to consider candidates who live on the East Coast, Boston or New York area, but will consider other areas for the right candidate. 
• Native-level fluency in English, both written and spoken

Mission

As a AI Translation Engineer, you will be at the forefront of advancing our platform’s translation capabilities. Leveraging your expertise in machine learning including applied research, fine tuning, benchmarking, and inference optimization, you will deliver high-performance solutions that power critical features for our growing client base.

Requirements

• Evaluation engineering: Translates product features into measurable user use cases; selects a small set of reliable offline metrics; builds benchmarks and runs slice analysis across languages and domains; designs rubrics and reference-less MT evaluation for ambiguous cases; defines online metrics and guardrails, runs A/B tests and staged rollouts, and validates that offline gains translate into production impact while spotting metric loopholes and regressions
  
  
• Data fluency: Owns end-to-end dataset pipelines: multi-source ingestion → cleaning/validation → scalable multilingual datasets, with versioning and auditability
  
  
• Classical NLP foundations: Understands MT/search text normalization and core matching approaches (exact/fuzzy, typo tolerance, multi-word + overlap handling, tries/Aho–Corasick/inverted index basics), and applies them to improve preprocessing and retrieval
  
  
• LLM operations: Operates external LLM providers end-to-end: designs prompts for structured generation/evaluation; builds automated filters/validators; generates synthetic data at scale using self-consistency and multi-provider “consilium”; selects and routes across providers based on quality–latency–cost; and uses self-hosted models when helpful for quality analysis/judging and provider fine-tuning data prep
  
  
• Practical production engineering: Can write lightweight services/jobs when needed and ship them with structured logs, monitoring/alerts, reliability basics (retries, idempotency, fallbacks), and scalability considerations; collaborates effectively with Dev/Platform

Key Responsibilities

• Develop and improve benchmarks for translation problems
• Work with NER, word alignment, automatic translation quality scoring system, and other problems
• Set up infrastructure for self-hosted solutions

Our technologies

• ML/Backend: Python, C#, PyTorch, Transformers
• DB: MongoDB, PostgreSQL, Elasticsearch
• Messaging: Apache Kafka
• Cloud: Amazon AWS
• Monitoring: ELK, Prometheus, Grafana

At JetBrains, code is our passion. Ever since we started, back in 2000, we’ve been striving to make the strongest, most effective developer tools on earth. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs. The ML Workflows Engineering team is dedicated to removing infrastructure challenges, streamlining machine learning operations (MLOps), and enabling teams to focus on the innovative work that matters most – building impactful ML models and intelligent agents. As part of the team, you'll play a key role in designing tools, automation, and pipelines that make machine learning development seamless and intuitive.

By integrating cutting-edge MLOps practices and engineering excellence, we aim to maximize productivity and remove the complexity of ML infrastructure so that our teams can push the boundaries of what’s possible in AI.

As part of our team, you will:

• Build tools, automation, and workflows to simplify infrastructure-heavy tasks, empowering AI teams to focus on experimentation and solving core challenges.
• Develop robust monitoring, logging, and tracing systems to ensure the performance and reproducibility of ML workflows in production.
• Design, implement, and maintain end-to-end machine learning pipelines to enable the seamless development, training, and deployment of ML models and intelligent agents.
• Work with large-scale distributed systems, including GPU clusters, to support training, fine-tuning, and evaluation of ML models.
• Collaborate with product and development teams to transform high-level goals into concrete, scalable, and maintainable systems.
• Optimize workflows for reproducibility, scalability, and cost-efficiency while keeping ML teams productive and focused on innovation.

We’ll be happy to have you on our team if you have:

• Hands-on experience with modern MLOps tooling, including Kubernetes, Cloud providers (GCP and AWS), and ML orchestration frameworks.
• A solid understanding of the ML lifecycle from idea to the customer-facing application.
• The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases.
• A customer-centric mindset – you care about how ML engineers are actually working and can translate their needs into actionable, scalable, and maintainable architectural decisions.
• Experience with modern CI/CD systems, like GitHub Actions or JetBrains TeamCity.
• At least three years of Python experience writing clean, maintainable code in modern ML codebases.

Our ideal candidate would have experience with:

• ML orchestrators and workflow tools such as ZenML, Dagster, and Airflow.
• Developing infrastructure components and services using cluster solutions like Kubernetes.
• The development of Python-based backend services.
• Creating and maintaining ML pipelines, including legacy ones.
• Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.

We’d be especially thrilled if you have experience with: 

• LLM inference frameworks such as vLLM, DeepSpeed, and TensorRT.
• Writing and maintaining Python libraries used by internal (or external) ML engineers.
• A strong theoretical background in NLP and transformer-based approaches.
• Writing code in Java and/or Kotlin.

#LI-HYBRID
#LI-MR1

At JetBrains, code is our passion. Ever since we started, back in 2000, we have strived to make the most effective developer tools on earth. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

The JetBrains Research team explores ways to use machine learning techniques and agentic approaches to help developers and enhance software development processes. Our work aims to improve the state of ML for code by turning the latest academic advances into practical applications.

Our team is looking for an ML Engineer to participate in a variety of projects in areas such as code completion and generation, AI agents development, and test generation. Though we don’t expect the candidate to have experience in all the tasks we work on, we are looking for someone excited to take on the challenge of working in a diverse set of contexts.

In this role, you will: 

• Design and prototype machine learning solutions to improve the software development workflow.
• Apply existing open-source models or train custom ones as needed.
• Build training and evaluation pipelines that support fast iteration and ensure reproducibility.
• Stay up to date with the latest advancements in applying machine learning to source code.
• Collaborate with colleagues and provide mentorship when appropriate.

We’ll be happy to have you on our team if you: 

• Have experience designing machine learning systems from scratch.
• Have a background in natural language processing or code modeling.
• Have a proven track record of delivering machine learning projects.
• Know how to design reproducible ML experiments.
• Enjoy working on projects with high uncertainty.
• Are proficient in Python, Java, or Kotlin.
• Have hands-on experience with modern ML frameworks.

It would be nice if you: 

• Have experience applying machine learning to programming languages.
• Have hands-on experience building and improving AI agents.
• Have experience in A/B testing and general knowledge of statistics
• Have worked with large-scale data processing pipelines

#LI-KP1

Our Machine Learning teams build with the latest tools and launch for Silicon Valley startups and Life Science giants alike.

Join our team to work remotely, ship projects you’re proud be a part of, and enjoy every other Friday off (yes, we really take it 😎)

The Role

• Understanding of statistical, ML and deep learning algorithms
• Analyze errors of models and design strategies to overcome them
• Deploy, maintain, and upgrade ML models and pipelines
• Ambition to learn and grow into different industries with a modern tech stack
• Autonomy, adaptability and positivity (fully remote and globally distributed team)

The Required Hard Skills

• Bachelor's Degree in Computer Science or related
• 2+ years of ML engineering experience
• Experience with Python, ML libraries and AI/ML frameworks (PyTorch, HuggingFace, TensorFlow, etc.)

• Excellent English - Being a global team, we work entirely in English for meetings, customer calls and business communications
• CV written in English

Bonus Points for Experience with…

• Building GenAI solutions, namely prompt engineering (e.g: Langchain), fine-tuning and serving LLMs, search and embeddings, etc.
• MLOps, favorably in AWS (e.g: Sagemaker) as well as standards tools (e.g: MLFlow)
• Visualizing and manipulating big datasets
• NLP

The Required Soft Skills

• Curiosity—Ambition to learn and grow into different industries with a modern tech stack
• Autonomy and positivity—We’re a fully remote, globally distributed team
• Teamwork—Enjoy a collaborative approach
• Adaptability—Operate with a startup mindset and move at a startup pace

The Benefits

• Every other Friday off (26 extra days off a year)
• Remote and flexible
• Explore and Relocation programs (three months work abroad or full international relo)
• Paid sick days and local holidays
• Business English classes program
• Continuous Learning Support
• Fitness and/or Mental Health Subscriptions
• Access to LokaLabs™, our internal research and development program
• Defined career path

Our Machine Learning teams build with the latest tools and launch for Silicon Valley startups and Life Science giants alike.

Join our team to work remotely, ship projects you’re proud be a part of, and enjoy every other Friday off (yes, we really take it 😎)

The Role

• Understanding of statistical, ML and deep learning algorithms
• Analyze errors of models and design strategies to overcome them
• Deploy, maintain, and upgrade ML models and pipelines
• Ambition to learn and grow into different industries with a modern tech stack
• Autonomy, adaptability and positivity (fully remote and globally distributed team)

The Required Hard Skills

• Bachelor's Degree in Computer Science or related
• 2+ years of ML engineering experience
• Experience with Python, ML libraries and AI/ML frameworks (PyTorch, HuggingFace, TensorFlow, etc.)

• Excellent English - Being a global team, we work entirely in English for meetings, customer calls and business communications
• CV written in English

Bonus Points for Experience with…

• Building GenAI solutions, namely prompt engineering (e.g: Langchain), fine-tuning and serving LLMs, search and embeddings, etc.
• MLOps, favorably in AWS (e.g: Sagemaker) as well as standards tools (e.g: MLFlow)
• Visualizing and manipulating big datasets
• NLP

The Required Soft Skills

• Curiosity—Ambition to learn and grow into different industries with a modern tech stack
• Autonomy and positivity—We’re a fully remote, globally distributed team
• Teamwork—Enjoy a collaborative approach
• Adaptability—Operate with a startup mindset and move at a startup pace

The Benefits

• Every other Friday off (26 extra days off a year)
• Remote and flexible
• Explore and Relocation programs (three months work abroad or full international relo)
• Paid sick days and local holidays
• Business English classes program
• Continuous Learning Support
• Fitness and/or Mental Health Subscriptions
• Access to LokaLabs™, our internal research and development program
• Defined career path