Localization Engineer Location: North Carolina, United States of America Full time Remote This is a fully remote role. Relocation assistance is NOT provided. What will you do in this role? Provides a full range of engineering and technical services on large and/or complex projects to analyze and prepare files to facilitate efficient and high-quality translations and implementation in the customer’s environment. Applies advanced technical expertise to ensure the highest quality and profitability on large and/or complex projects. Qualification requirements: Education requirements - Bachelor's degree is required, preferably in Translation Studies, Linguistics, Programming, Computer Science or similar fields. Experience requirements - Previous Localization Engineering experience is required. - Previous experience that provides the knowledge, skills, and abilities to perform the job (comparable to 5 years) - Experience working with various file formats, such as Json, Resx, Vtt, HTML, XML (all different types of XML from different systems), Madcap Flare, Dita, Articulate, mp4, Captivate, etc. is required. - Experience working with CAT/TMS tools, such as Wordbee, SDL Trados Studio, MemoQ, Memsource/Phrase, XTM, etc. is required. - Experience in troubleshooting localization and translation projects throughout the whole lifecycle is required. - Experience with Content Management Systems (Tridion, ST4) is preferred. Knowledge, Skills, Abilities - Performs and manages localization engineering tasks - Ability to convert the different formats to translatable form and then back-convert the translatable files into the original formats - Experience with parsers/scripting - Guides project team and/or external engineers on the various application conversion steps and raises questions/queries clearly - Helps resolve/fix tag issues in the translated files - Experience/knowledge in programming languages (Python, C++, C#) - Shares ideas for process automation or new tool suggestions - Updates/adds new configuration/settings in TMS/CAT Tool based on new process updates/requirements - Ability to work in a fast-paced environment - Ability to deliver under tight schedules - Strong communication and collaboration skills - Ability to manage multiple projects and meet deadlines - Knowledge of languages and cultural nuances - Certification in localization engineering or related fields (preferred) *Must be legally authorized to work in the US without sponsorship.

• You’ll take ownership of how product data is structured, validated, and used across the platform. • Improving how we classify and understand products at scale • Raising the overall quality of catalogue data and defining what “good” looks like • Ensuring product data supports effective search, filtering, and discovery • Identifying gaps in our catalogue and surfacing opportunities for growth • Improving how our catalogue performs across external channels • You’ll build and evolve the systems and decision logic that enable this, and iterate based on real-world performance and data. • You’ll work across: - Structured data (catalogue attributes, GTINs, taxonomy) - Unstructured data (text and images) - Behavioural data (search, clicks, conversions)

Senior MLOps Engineer Toronto, Ontario Engineering / Full-time / Hybrid About Us Deep Genomics is at the forefront of using artificial intelligence to transform drug discovery. Our proprietary AI platform decodes the complexity of RNA biology to identify novel drug targets, mechanisms, and therapeutics inaccessible through traditional methods. With expertise spanning machine learning, bioinformatics, data science, engineering, and drug development, our multidisciplinary team in Toronto and Cambridge, MA is revolutionizing how new medicines are created. Opportunity Join us in building the future of AI-driven drug discovery as a Senior MLOps Engineer. You will own and evolve the infrastructure that powers our ML pipelines – from cloud environments and CI/CD systems to workflow orchestration and model deployment. You will work closely with ML scientists, bioinformaticians, and software engineers to keep our platform reliable, reproducible, and scalable. Ideal Candidate You are someone who enjoys keeping the infrastructure running smoothly so that scientists can focus on their research. You are comfortable working across cloud platforms, CI/CD systems, containers, and GPUs – and you take pride in making these systems reliable and easy for others to use. You have 4+ years of experience in production infrastructure or MLOps, you write solid Python, and you are curious about the ML and scientific workflows your work supports. Above all, you are a collaborative, kind team member who communicates clearly, adapts to evolving needs, and is happy to help colleagues grow their own infrastructure skills along the way. If this sounds like you, we would love to hear from you. Key Responsibilities - Maintain and improve cloud infrastructure (GCP) using Infrastructure-as-Code tools (Terraform). - Manage IAM, RBAC, and permission policies across cloud environments. - Own and evolve CI/CD pipelines (CircleCI, GitHub Actions) and ensure best practices are followed across the engineering and ML teams. - Administer and support workflow orchestration platforms (e.g., Seqera/Nextflow, Argo, Kubeflow). - Operate and configure ML experiment tracking and registry tooling (e.g., W&B, MLflow). - Build and maintain containerized environments (Docker) and manage Kubernetes clusters. - Manage GPU resources – provisioning, scheduling, and debugging hardware and driver issues. - Write and maintain Python tooling, scripts, and integrations that support ML infrastructure. - Help deploy ML models to production environments and monitor their performance. Basic Qualifications - 4+ years of experience operating production infrastructure. - Proficiency with cloud platforms (GCP preferred; AWS/Azure acceptable) and Infrastructure-as-Code (Terraform). - Extensive Hands-on experience with Kubernetes and containerization (Docker). - Solid background in CI/CD systems (CircleCI, GitHub Actions, or similar). - Experience managing GPU compute (provisioning, debugging, driver management). - Familiarity with Python package and environment management (e.g., pip, conda, pixi). - Strong Python programming skills. - Self-motivated problem solver with excellent communication skills. Preferred Qualifications - Understanding of ML frameworks (e.g., PyTorch, PyTorch Lightning), ML workflows (training, inference, evaluation), and the model lifecycle. - Familiarity with MLOps tooling (e.g., W&B, Ray, VertexAI) and distributed compute patterns (e.g., DDP, realtime/batch inference, multi-node training). - Familiarity with Kubernetes CRDs and batch/gang schedulers (e.g., Volcano, Kueue). - Experience working with large-scale datasets (storage, versioning, efficient access patterns). - Experience working directly with scientists and researchers in an interdisciplinary setting. - Knowledge of biology and/or machine learning science. - Familiarity with data compliance and governance frameworks (e.g., HIPAA, SOC 2). - Previous startup experience. What We Offer - A collaborative and innovative environment at the frontier of computational biology, machine learning, and drug discovery. - Highly competitive compensation, including meaningful stock ownership. - Comprehensive benefits - including health, vision, and dental coverage for employees and families, employee and family assistance program. - Flexible work environment - including flexible hours, extended long weekends, holiday shutdown, unlimited personal days. - Maternity and parental leave top-up coverage, as well as new parent paid time off. - Focus on learning and growth for all employees - learning and development budget & lunch and learns. - Facilities located in the heart of Toronto - the epicenter of machine learning and AI research and development, and in Kendall Square, Cambridge, Mass. - a global center of biotechnology and life sciences. Deep Genomics encourages applications from all backgrounds who seek the opportunity to build the world's leading AI-driven genetic medicine company. If you have a disability or special need, accommodation is available on request for candidates taking part in all aspects of the selection process. *This posting reflects a current vacancy. We offer competitive compensation aligned with local market benchmarks. The salary range for this role is $175,000 - $200,000, and reflects Canada-based roles; compensation may differ for U.S.-based candidates.

• Invent and productionize Transformer/RAG/Graph RAG architectures that surface the right contact, company, or insight while driving quantization, distillation, and SLM fine-tuning (GTE-Qwen, modernBERT) so models stay fast and affordable at petabyte scale • Prototype and launch hybrid dense/sparse retrieval pipelines on vector DBs to build language-agnostic clustering and classification systems that power our intelligence layer • Own high-recall NER models that tag people, orgs, locations, and industry-specific entities across multi-language text, extracting structured insights from web data to improve our signal detection capabilities • Build cross-dataset entity-resolution frameworks that dedupe and merge hundreds of millions of fragmented company and person records with sub-second latency, creating enriched, unified entities enhanced with knowledge-graph signals • Design and implement agentic workflows with robust evaluation frameworks focused on NER and entity resolution tasks, including large-scale A/B and back-testing plans that close the loop from experiment to KPI uplift • Scale ML solutions and drive cross-functional impact by partnering with ML engineers to ensure production reliability, translating product goals into measurable ML KPIs, and influencing roadmap and investment decisions while mentoring junior scientists and engineers • Drive end-to-end project ownership from problem definition through deployment, collaborating closely with engineering and product teams to understand business requirements and translate them into scalable ML solutions that enhance foundation data quality across company firmographics, professional demographics, C-suite profiles, and web-extracted signals