As a Platform Engineer, you will build and evolve the platform that takes our machine learning models from research to reliable, scalable production. Working at the intersection of machine learning, infrastructure, and software engineering, you will partner closely with ML engineers to improve performance, reliability, cost efficiency, and the overall maturity of our ML platform.

If you enjoy designing robust systems, automating complex workflows, and optimising ML platforms for performance, reliability, and cost, this role is for you.

What you will be doing

• Designing, building, and maintaining scalable ML platforms, tools, and infrastructure that enable teams to efficiently train, experiment with, and evaluate machine learning models
• Modernising the ML platform by migrating workloads from SGE Grid Engine to Kubernetes‑based orchestration systems such as Slurm or Ray, improving scalability, reliability, and efficiency of ML workflows.
• Modernising the ML workflows with cutting-edge tools for pipeline orchestration, experiment tracking, observability, and model versioning.
• Optimising ML infrastructure to maximise GPU utilisation, streamline scheduling, and enhance distributed training performance.
• Building and maintaining CI/CD workflows for ML systems, including model versioning, testing, and release automation.
• Collaborating closely with ML engineers to ensure models are deployable, reproducible, and production-ready.
• Continuously evaluating new tools, frameworks, and approaches to improve the efficiency, reliability, and maturity of the ML platform.
• Promoting operational excellence by reducing manual processes, improving automation, and raising the overall platform reliability and efficiency.
• Contributing to the ML platform roadmap, tooling standards, and best practices to support excellence in Voice AI.

Who we are looking for:

• Python skills for production systems
• Proven ability to optimise model training and inference, including GPU-based and distributed workloads
• Experience with containerisation and orchestration tools (e.g., Docker, Kubernetes)
• Strong background in monitoring, observability, and reliability for ML platforms
• Hands-on experience across cloud, on-premises, and hybrid infrastructure environments
• Solid understanding of ML fundamentals, including training, evaluation, and lifecycle management
• Familiarity with CI/CD, version control, reproducible workflows, and ML frameworks (e.g., PyTorch, TensorFlow)
• Experience with large-scale data/AI platforms, experiment tracking, and governance, plus strong cross-team collaboration skills

At Tenstorrent, we believe the future of computing must be open, which is why our interns don’t just watch from the sidelines - they help build the core of it. We provide a "code-to-career" pipeline where students collaborate with industry experts to solve high-stakes problems in RISC-V and AI hardware-software co-design. By joining us, you are taking an internship to democratize high-performance computers that are accessible to everyone.

As an intern at Tenstorrent, you will experience working on a carefully defined and real engineering project, guided by a dedicated mentor and a manager, and as a part of a team. This role focuses on model training pipelines on the Tenstorrent compiler stack and how neural networks are brought up and optimized on our hardware. We are looking for a minimum of 3 months for this role with the potential for extension to 6 months. 

This role is on-site based in Belgrade, Serbia.

Who You Are

• Possessing or currently pursuing a Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field.
• Strong understanding of neural network fundamentals.
• Proficiency in Python; experience with C++ is a plus.
• Ability to learn and work effectively within a team environment.

What We Need

• Participate in the implementation and validation of model training pipelines on Tenstorrent compiler stack, specifically using PyTorch and JAX.
• Support benchmarking, analysis, and performance optimization of various model architectures.
• Contribute to efforts in scaling out distributed model training under guidance and participate in discussions related to the co-design of Tenstorrent's hardware and software stack.
• Assist in the integration of Tenstorrent software with widely used tools in the machine learning community.

What You Will Learn

• The process of getting neural network models to train correctly and efficiently on our hardware.
• Techniques for benchmarking, analysis, and performance optimization of various model architectures.
• How to scale out distributed model training and participate in hardware–software co-design discussions.
• How Tenstorrent software integrates with widely used tools in the machine learning community and how a cross‑functional engineering team collaborates on real projects.

Hiring Timelines

This internship opportunity is available throughout our 3 terms with the following corresponding recruitment cycles:

• Winter Term: Mar–May work term, Nov–Jan recruit.
• Summer Term: Aug–Oct work term, Jan–May recruit.
• Fall Term: Oct–Dec work term, Apr–May recruit.

Please note these timelines are for reference only. Actual timelines may vary.

Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.

At Tenstorrent, we believe the future of computing must be open, which is why our interns don’t just watch from the sidelines - they help build the core of it. We provide a "code-to-career" pipeline where students collaborate with industry experts to solve high-stakes problems in RISC-V and AI hardware-software co-design. By joining us, you are taking an internship to democratize high-performance computers that are accessible to everyone. 

In this role, you will implement state of art ML models on Tenstorrent hardware using Python and C++, focusing on pushing both accuracy and inference speed. You will work hands-on with Tenstorrent’s open-source software stack (tt-metalium, tt-nn, tt-llk), taking models from framework to silicon and iterating on performance. You will own a well-defined engineering project under the guidance of a dedicated mentor, with direct impact on how real workloads run on our chips. We are looking for a minimum of 3 months for this role with the potential for extension to 6 months. 

This role is onsite, based in our Belgrade office.

Who You Are

• Enrolled in the final year of BSc or MSc studies in Computer Science, Computer Engineering, Software Engineering, Electronics, Math, or a related field.
• Solid coding skills in Python and C++, with a basic understanding of machine learning concepts and frameworks.
• You have a passion for programming, are eager to learn, and enjoy solving complex performance and optimization problems.
• You are collaborative, open to feedback, and excited to work closely with experienced engineers and a dedicated mentor.

What We Need

• Implement functional ML models on Tenstorrent hardware using Python and popular ML frameworks like PyTorch.
• Benchmark, analyze, and optimize the performance of the implemented model's inference using existing tools and coding in C++ and Python.
• Collaborate with experienced engineers to validate the accuracy of implemented models and iterate on improvements.
• Contribute to performance optimization efforts where success is measured by achieving both high accuracy and fast execution (inference) of ML models on Tenstorrent hardware.

What You Will Learn

• How to implement state-of-the-art ML models on Tenstorrent hardware using Python, C++, and popular ML frameworks like PyTorch.
• Techniques for benchmarking, analyzing, and optimizing the performance of ML model inference using existing tools and code in C++ and Python.
• How to use (and potentially debug and fix) Tenstorrent’s open-source software libraries, such as tt-metalium, tt-nn, and tt-llk.
• How to collaborate with experienced engineers, apply various problem-solving techniques, and drive a well-defined engineering project under the guidance of a dedicated mentor.

Hiring Timelines

This internship opportunity is available throughout our 3 terms with the following corresponding recruitment cycles:

• Winter Term: Mar–May work term, Nov–Jan recruit.
• Summer Term: Aug–Oct work term, Jan–May recruit.
• Fall Term: Oct–Dec work term, Apr–May recruit.

Please note these timelines are for reference only. Actual timelines may vary.

Join the team revolutionizing AI computing at Tenstorrent. You'll work on TT-Forge, our MLIR-based compiler that enables developers to run AI on all configurations of Tenstorrent hardware using an open-source, performant, and general-purpose compiler. You will be at the forefront of the AI hardware revolution, building compiler technologies that redefine what’s possible.

This role is hybrid based in Belgrade, Serbia.

We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.

Who You Are:

• A passionate software engineer eager to work on compiler technologies and the challenges of AI hardware, whether from compilers, systems, or broader software backgrounds.
• Fluent in C++ and Python, with experience building complex systems that bridge high-level frameworks to low-level execution.
• Excited by compiler optimization and machine learning, with experience in PyTorch, JAX, TensorFlow, or deep systems programming.
• A collaborative problem-solver who thrives in open-source and enjoys working closely with hardware and software engineers.
  
  
  

What We Need:

• A drive to solve novel challenges in AI compilation, from optimizing computational graphs to creating custom dialects and transformation passes.
• Experience or strong interest in MLIR and how modular compiler frameworks connect AI models to advanced hardware.
• Motivation to build technology that impacts the future of AI, knowing your work will enable the next wave of breakthroughs.
  
  
  

What You Will Learn:

• How to build open-source compiler frameworks supporting diverse AI models and workloads, including training and multi-chip scaling.
• Deep expertise in compiler technologies including custom MLIR dialects (TTIR, TTNN, TTKernel) and transformation passes.
• New methods for human-in-the-loop compiler optimization using TT-Explorer, making advanced tuning tools usable by all developers.
• How compiler technology powers Tenstorrent’s mission to deliver affordable, open-source AI computing in a highly competitive space.

Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.

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 Incode

Incode is a Series B unicorn rewriting how the world proves identity. Our AI-powered platform lets leading banks, fintechs, marketplaces, and governments deliver friction-free experiences while defeating fraud and safeguarding privacy. Customers such as Citi, AirBnB, Block, Chime, Sixt, and TikTok rely on Incode to power their identity verification and security.

Recently named a Leader in the Gartner® Magic Quadrant™ for Identity Verification, we’re scaling fast - and we’re looking for world-class Machine Learning Engineers to help us keep pushing the boundaries of biometric technology.

The Impact You’ll Make

As a Machine Learning Engineer at Incode, you’ll design, build, and deploy the deep learning models that power our most advanced technologies — from facial recognition and liveness detection to document processing and ID validation.

You’ll join a highly specialized engineering team working on solving complex, real-world challenges with cutting-edge ML architectures. Your work will directly influence how people verify their identity safely, seamlessly, and securely around the world.

What You’ll Own & Drive

• Innovate & Optimize - Develop and refine state-of-the-art deep learning models for computer vision applications such as facial recognition, liveness detection, and document processing and validation.

• Production-Ready Solutions - Architect and maintain scalable, efficient ML pipelines and ensure production-readiness and cost efficiency of deployed models.

• Research & Discovery - Stay at the forefront of academic and industry breakthroughs, experiment with emerging architectures and algorithms, and translate research into production-grade solutions.

• Data Mastery - Drive improvements in model performance through robust data preprocessing, cleaning, and analysis workflows.

• Collaborate & Communicate - Partner closely with research, product, and engineering teams to integrate ML solutions seamlessly into Incode’s production environment.

• Mentorship & Leadership - Provide technical guidance, share best practices, and contribute to building a culture of innovation and continuous learning.

Your Background

• 5+ years of industrial experience in Machine Learning, Deep Learning, or Computer Vision.

• Expertise in Python and deep learning frameworks such as PyTorch or TensorFlow.

• Demonstrated experience developing, deploying, and optimizing models for both performance and cost efficiency in production environments (including edge AI).

• Specialized expertise in at least one of the following areas:

  • Facial Recognition – face recognition, age estimation, facial attributes analysis.

  • Liveness Detection – facial authentication, anti-spoofing, and anti-deepfake defense.

  • Document Processing – OCR-based extraction, document analysis, and quality assurance.

  • ID Verification – biometric data authentication and document integrity validation.

• Proven ability to design and maintain end-to-end ML pipelines.

• Track record of research contributions or publications in deep learning and computer vision.

• Excellent communication and collaboration skills across cross-functional teams.

• Passion for mentoring talent and thriving in both long-term research and rapid iteration cycles.

The Qualities That Set You Apart

• Deep curiosity and technical excellence in AI and computer vision.

• Builder mindset with a drive to deliver real-world impact through applied research.

• Strong analytical thinking balanced by practical execution.

• Collaborative spirit and ability to simplify complex ideas for diverse audiences.

• Commitment to innovation, scalability, and continuous improvement.

Why Incode?

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

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. Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs.

We’re building multi-step coding agents that can understand large codebases, plan changes, call tools, and iterate with the user. As a Research Engineer in the Agentic Models team, you’ll be responsible for the models, training loops, and evaluation pipelines that power these agents.

You’ll work at the intersection of SFT and RL-style post-training, and product-driven evaluation, using our distributed GPU and MapReduce clusters to ship models into JetBrains products.

As part of our team, you will:

• Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents.
• Train and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs.
• Build and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks.
• Design evaluation frameworks and metrics for agent behavior, analyze traces and logs, and close the loop from evaluation back into training, data, and reward design.
• Analyze training and evaluation results to propose and implement improvements to model architectures, training recipes, and datasets.
• Work with large-scale infrastructure, including distributed training on GPU clusters and large MapReduce-style data processing for pre-training and fine-tuning datasets.
• Collaborate closely with research, product, and infrastructure teams to turn high-level product visions into concrete models, experiments, and shipped features. 

We’ll be happy to bring you on board if you have:

• Extensive hands-on experience training LLMs (pre-training, fine-tuning, or post-training) in a research or production setting.
• Deep expertise in modern deep learning frameworks such as PyTorch, and specialized LLM training stacks (e.g. Megatron, NeMo, verl, or similar).
• Strong theoretical and practical understanding of LLM fundamentals: architectures, tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs.
• 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 product-aware mindset – you care about how developers actually use agents and can translate product needs and failure modes into modeling and evaluation work.
• At least 3 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 Kubeflow, Dagster, Airflow, ZenML, and/or job schedulers like Kubernetes or SLURM.
• Large-scale data and training pipelines, e.g. MapReduce-style clusters, multi-node GPU training, or workloads on the order of 1M+ CPU/GPU hours.
• Designing and maintaining evaluation pipelines for LLMs or agents, including metrics, dashboards, experiment tracking, and automated regression checks.
• AI agent development, such as tool-using agents, planners, or multi-step coding workflows, and familiarity with agentic frameworks or patterns.
• Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.
• Inference optimization and serving optimized models in production.

#LI-KP1

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. Today, AI-powered coding agents are becoming a core part of how developers write Kotlin – and we want to make sure they write it well.

The Kotlin AI Value Stream team is responsible for how AI agents understand, generate, and improve Kotlin code across all platforms: Android, Kotlin Multiplatform, server-side, web, desktop, and others. We build the evaluation infrastructure, error analysis tools, and post-training pipelines that measure and improve agent behavior on real Kotlin developer tasks.

As a Research Engineer on this team, you'll own the end-to-end loop: Analyze how agents fail on Kotlin → build evals that capture those failures → research and implement methods to fix them → measure the improvement. Your work will directly shape how millions of developers experience Kotlin through AI coding agents.

As part of our team, you will:

Build tools for agentic error analysis

• Design and implement tooling to systematically capture, classify, and analyse errors that AI coding agents make when generating Kotlin code.
• Build observability pipelines over agentic traces – mining patterns from agent sessions in JetBrains IDEs, Junie, Claude Code, Cursor, and other coding agents.

Build evaluation pipelines

• Design, implement, and maintain evaluation pipelines that measure Kotlin code generation quality across dimensions, including correctness, idiomaticity, build success, framework usage, and test coverage.
• Build simulation environments where coding agents can be measured on realistic Kotlin developer tasks – from greenfield KMP projects and Gradle dependency management to migrating Spring applications from Java to Kotlin.
• Own evaluation infrastructure: metrics, experiment tracking, automated regression checks, and reproducible benchmarking.

Research methods for improving agent and model behavior on Kotlin

• Experiment with post-training techniques (SFT, DPO, GRPO) to improve how models handle Kotlin-specific patterns, idioms, and frameworks.
• Investigate context engineering approaches: CLAUDE.md/AGENTS.md files, compiler-as-verifier feedback loops, Kotlin LSP integration, and MCP-based tooling.
• Run experiments to measure impact: A/B comparisons, benchmark suites, and before/after analyses on real codebases.
• Collaborate with model providers (Anthropic, OpenAI, and Google) to translate Kotlin-specific findings into model improvements.

Build public Kotlin benchmarks

• Design and build open-source benchmarks that measure AI coding agent performance on Kotlin tasks and eventually become the standard reference for the ecosystem.
• Create task datasets covering the breadth of Kotlin usage: the server side (Spring, Ktor), multiplatform projects (KMP), build systems (Gradle), Android, library development, and others.
• Include both mined real-world tasks and carefully designed synthetic tasks that test specific Kotlin capabilities.
• Maintain and evolve benchmarks as models improve, ensuring they remain challenging, relevant, and contamination-resistant.

We'll be happy to have you on board if you have:

• Hands-on experience building evaluation or analysis pipelines for LLMs or AI coding agents in a research or production setting.
• Strong Python engineering skills (at least three years), with the ability to write clean, maintainable code in data-heavy and ML-adjacent codebases.
• Experience with data analysis at scale: querying large datasets (SQL/Athena), building data pipelines, and performing statistical analysis of experimental results.
• The ability to own projects end to end – from identifying a problem in agent traces to designing an eval, running experiments, and shipping a fix.
• A product-aware mindset: You care about how agents are actually used by developers and can translate real failure modes into evaluation and training work.
• Familiarity with Kotlin or a strong willingness to develop deep Kotlin expertise (you'll be living in Kotlin codebases daily).

Our ideal candidate would also have experience with:

• Post-training LLMs: SFT, RLHF, DPO, GRPO – either hands-on training or designing the data and reward pipelines that feed into training.
• Modern deep learning frameworks (PyTorch) and LLM training stacks (TRL, verl, Megatron, or similar).
• AI agent development: tool-using agents, multi-step coding workflows, agentic frameworks.
• Evaluation frameworks and tools: Inspect AI, Promptfoo, LM-evaluation-harness, or custom eval pipelines.
• Experiment tracking and observability: Weights & Biases, MLflow, Langfuse, or similar.
• The Kotlin ecosystem: Android, Gradle, KMP, Spring, Ktor – with an understanding of the developer workflows that agents need to support.
• Contributing to or maintaining open-source projects, especially benchmarks or evaluation tools.

Don't check every box? That's okay – if you're excited about this work and bring strong fundamentals, we'd love to hear from you. We're happy to talk and provide the training you need to grow into the role.

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.
• Relocation support. We help make your move as smooth and stress-free as possible. 
• 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

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

Years from now, history will look back at this moment as a landmark for the human race—the moment when generative artificial intelligence began to change everything. Luckily, you happen to be living through it. Now what? 

If you’re one of those rare people driven to add their intellectual energy to the relentless momentum of technological advancement, consider Loka your optimal leverage point. 

Loka, a globally distributed tech consultancy based in Silicon Valley, is seeking a Senior Machine Learning Engineer to join our growing team. In this position you will serve as the primary specialist in designing and implementing machine learning solutions across multiple projects. Your expertise in ML algorithms and ML, DL and Foundation Models will empower us to develop robust, scalable systems and improve them over time. Ultimately we expect you to not just fit in with our ML crew but to elevate it to new heights. 

Our technical teams help clients launch innovations that fix the climate, defeat cancer, improve agricultural efficiencies, teach kids to read and more. We work with all kinds of companies, from Silicon Valley startups to major household brands, always guided by our fundamental belief that mission matters. 

We count more than 100 certified specialists, technical experts and PhDs among our teammates—committed Lokals who contribute to their brilliance not only to serving our clients but to mentoring junior colleagues as well. We work entirely remotely, test new ideas with an in-house incubator called LokaLabs, observe local holidays and take every other Friday off (really). We’re as proud of our culture as we are of our projects. You will be too. 

The Role

• Take full responsibility for assigned tasks and projects, ensuring accountability for outcomes and results.
• Understand business objectives and develop models that help achieve them, along with metrics to track progress.
• Design and implement complex ML systems using classical machine learning, deep learning and foundation models, adhering to best practices.
• Lead client communications by gathering requirements, managing expectations and communicating deliverables effectively.
• Wrangle, explore and visualize data with a keen eye for issues requiring data cleaning, as well as differences in data distribution that may affect post-deployment performance.
• Analyze model errors and design strategies to overcome them.
• Deploy, maintain and upgrade ML models and pipelines.
• Follow Loka’s career track for growth by demonstrating technical excellence, innovation, autonomy, ownership, communication and teamwork.
• Drive architectural decisions and contribute to high-level planning and strategy. 
• Direct report to ML leads.
• Stay updated with industry trends to incorporate cutting-edge techniques and tools into projects.
• Write and maintain technical documentation.
• Provide guidance and mentorship to junior ML engineers within the team.
• Collaborate cross-functionally with other teams (Data Engineering, DevOps, Backend) to ensure seamless integration of ML solutions.
• Continuously optimize models for performance, scalability and cost-effectiveness, especially within the AWS environment.

Required Hard Skills 

• 4+ years of AI/ML engineering experience
• Bachelor’s degree in Computer Science or related
• Experience with Python, ML libraries and AI/ML frameworks (sklearn, PyTorch, HuggingFace, TensorFlow, Transformers, LangChain/LangGraph, Llamlndex, smolagents, strands-agents etc.) 
• Experience building GenAI solutions through prompt engineering, fine-tuning and developing agents with common patterns like Agentic RAG, NLQ etc.
• Client-facing experience
• Familiarity with containerization and orchestration tools 

Additional Requirements 

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

Not Required but Nice to Have

• Experience with MLOps/LLMOps, preferably in AWS (e.g: Sagemaker/Bedrock), as well as standards tools (e.g. MLFlow, LangFuse) 
• Experience in consultancy environments or startups, including working directly with clients, managing projects and adapting to different settings. - Plus

Required Soft Skills 

• Curiosity: Strive 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. 

Benefits

• Every other Friday off (26 extra days off a year)
• Remote-first culture
• 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

Your achievements matter to us! Ensure your CV and LinkedIn and GitHub profiles are up to date and accurately reflect your experience. 

Years from now, history will look back at this moment as a landmark for the human race—the moment when generative artificial intelligence began to change everything. Luckily, you happen to be living through it. Now what? 

If you’re one of those rare people driven to add their intellectual energy to the relentless momentum of technological advancement, consider Loka your optimal leverage point. 

Loka, a globally distributed tech consultancy based in Silicon Valley, is seeking a Senior Machine Learning Engineer to join our growing team. In this position you will serve as the primary specialist in designing and implementing machine learning solutions across multiple projects. Your expertise in ML algorithms and ML, DL and Foundation Models will empower us to develop robust, scalable systems and improve them over time. Ultimately we expect you to not just fit in with our ML crew but to elevate it to new heights. 

Our technical teams help clients launch innovations that fix the climate, defeat cancer, improve agricultural efficiencies, teach kids to read and more. We work with all kinds of companies, from Silicon Valley startups to major household brands, always guided by our fundamental belief that mission matters. 

We count more than 100 certified specialists, technical experts and PhDs among our teammates—committed Lokals who contribute to their brilliance not only to serving our clients but to mentoring junior colleagues as well. We work entirely remotely, test new ideas with an in-house incubator called LokaLabs, observe local holidays and take every other Friday off (really). We’re as proud of our culture as we are of our projects. You will be too. 

The Role

• Take full responsibility for assigned tasks and projects, ensuring accountability for outcomes and results.
• Understand business objectives and develop models that help achieve them, along with metrics to track progress.
• Design and implement complex ML systems using classical machine learning, deep learning and foundation models, adhering to best practices.
• Lead client communications by gathering requirements, managing expectations and communicating deliverables effectively.
• Wrangle, explore and visualize data with a keen eye for issues requiring data cleaning, as well as differences in data distribution that may affect post-deployment performance.
• Analyze model errors and design strategies to overcome them.
• Deploy, maintain and upgrade ML models and pipelines.
• Follow Loka’s career track for growth by demonstrating technical excellence, innovation, autonomy, ownership, communication and teamwork.
• Drive architectural decisions and contribute to high-level planning and strategy. 
• Direct report to ML leads.
• Stay updated with industry trends to incorporate cutting-edge techniques and tools into projects.
• Write and maintain technical documentation.
• Provide guidance and mentorship to junior ML engineers within the team.
• Collaborate cross-functionally with other teams (Data Engineering, DevOps, Backend) to ensure seamless integration of ML solutions.
• Continuously optimize models for performance, scalability and cost-effectiveness, especially within the AWS environment.

Required Hard Skills 

• 4+ years of AI/ML engineering experience
• Bachelor’s degree in Computer Science or related
• Experience with Python, ML libraries and AI/ML frameworks (sklearn, PyTorch, HuggingFace, TensorFlow, Transformers, LangChain/LangGraph, Llamlndex, smolagents, strands-agents etc.) 
• Experience building GenAI solutions through prompt engineering, fine-tuning and developing agents with common patterns like Agentic RAG, NLQ etc.
• Client-facing experience
• Familiarity with containerization and orchestration tools 

Additional Requirements 

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

Not Required but Nice to Have

• Experience with MLOps/LLMOps, preferably in AWS (e.g: Sagemaker/Bedrock), as well as standards tools (e.g. MLFlow, LangFuse) 
• Experience in consultancy environments or startups, including working directly with clients, managing projects and adapting to different settings. - Plus

Required Soft Skills 

• Curiosity: Strive 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. 

Benefits

• Every other Friday off (26 extra days off a year)
• Remote-first culture
• 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

Your achievements matter to us! Ensure your CV and LinkedIn and GitHub profiles are up to date and accurately reflect your experience.