{"schemaVersion":"jobsearcher.job.v1","id":"35bd02fe02310954dcea3b8b","url":"https://jobsearcher.com/jobs/35bd02fe02310954dcea3b8b","canonicalUrl":"https://jobsearcher.com/jobs/35bd02fe02310954dcea3b8b","title":"Senior Machine Learning Engineer","description":"About the role:\nWe're looking for a Machine Learning Engineer who can own the full lifecycle of alarge language model in production — from training and fine-tuning throughdeployment at scale and rigorous evaluation of what actually ships. This isn't aresearch-only role and it isn't a pure infrastructure role: you'll need to begenuinely fluent in all three, because the hardest problems in this space live atthe seams between them — a training decision that breaks serving latency, adeployment optimization that silently degrades output quality, an eval result thatdoesn't predict real-world behavior.\nWhat you'll do:\nTraining & fine-tuning\n- Design and execute pretraining, continued pretraining, and fine-tuning runs (SFT, DPO/RLHF-style alignment, LoRA/QLoRA and full-parameter approaches) against clear, measurable objectives\n- Own data pipeline decisions that materially affect model quality — curation, deduplication, mixture weighting, and contamination checks against eval sets- Run and interpret distributed training (multi-GPU, multi-node) using frameworks such as FSDP, DeepSpeed, or Megatron-style parallelism, and diagnose failures that only show up at scale (loss spikes, stragglers, checkpoint corruption)\n- Make and defend real tradeoffs between model size, training cost, and downstream performance\nDeployment\nTake a trained model to production: quantization, batching strategy, KV-cache management, and serving framework selection (e.g. vLLM, TensorRT-LLM, TGI) with explicit latency/throughput/cost targets, not just \"make it run\"\nDesign for the failure modes specific to LLM serving — tail latency under load, graceful degradation, prompt injection surface area, and safe fallback behavior\nBuild the operational muscle around this: monitoring, alerting, and rollback paths for a model in production, treated with the same rigor as any other critical service, not as a one-off notebook export\nAssessment & evaluation\nBuild and maintain evaluation harnesses that go beyond running published benchmarks — including task-specific eval sets that reflect your actual product's use cases, not just leaderboard performance\nDesign human evaluation protocols where automated metrics fall short, and know which is which\nOwn regression detection: catching quality drops introduced by a new checkpoint, a prompt template change, or a serving optimization before they reach users- Contribute to safety and robustness evaluation — hallucination rate, adversarial/ red-team testing, and behavior under distribution shift — as a first-class part of the release process, not an afterthought\nWhat we're looking for\n4+ years of applied ML engineering experience, with at least **2 years working directly on large language models in a production context\nReal production deployment experience — you've shipped a model that served live traffic, and you can talk about the latency/cost/quality tradeoffs you made- Strong software engineering fundamentals\nFluency with the modern LLM tooling landscape (training frameworks, serving frameworks, eval tooling)\nComfort with ambiguity — you'll be asked to define what \"good\" means for a model behavior that doesn't have an established benchmark\nPay: $160,000.00 - $260,000.00 per year\nWork Location: Remote","company":"Placement Force","rawCompany":"placement force","city":"Remote","state":"OR","isRemote":false,"isActive":false,"createdAt":"2026-08-05T00:13:46.611Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Machine Learning Engineer","description":"About the role:\nWe're looking for a Machine Learning Engineer who can own the full lifecycle of alarge language model in production — from training and fine-tuning throughdeployment at scale and rigorous evaluation of what actually ships. This isn't aresearch-only role and it isn't a pure infrastructure role: you'll need to begenuinely fluent in all three, because the hardest problems in this space live atthe seams between them — a training decision that breaks serving latency, adeployment optimization that silently degrades output quality, an eval result thatdoesn't predict real-world behavior.\nWhat you'll do:\nTraining & fine-tuning\n- Design and execute pretraining, continued pretraining, and fine-tuning runs (SFT, DPO/RLHF-style alignment, LoRA/QLoRA and full-parameter approaches) against clear, measurable objectives\n- Own data pipeline decisions that materially affect model quality — curation, deduplication, mixture weighting, and contamination checks against eval sets- Run and interpret distributed training (multi-GPU, multi-node) using frameworks such as FSDP, DeepSpeed, or Megatron-style parallelism, and diagnose failures that only show up at scale (loss spikes, stragglers, checkpoint corruption)\n- Make and defend real tradeoffs between model size, training cost, and downstream performance\nDeployment\nTake a trained model to production: quantization, batching strategy, KV-cache management, and serving framework selection (e.g. vLLM, TensorRT-LLM, TGI) with explicit latency/throughput/cost targets, not just \"make it run\"\nDesign for the failure modes specific to LLM serving — tail latency under load, graceful degradation, prompt injection surface area, and safe fallback behavior\nBuild the operational muscle around this: monitoring, alerting, and rollback paths for a model in production, treated with the same rigor as any other critical service, not as a one-off notebook export\nAssessment & evaluation\nBuild and maintain evaluation harnesses that go beyond running published benchmarks — including task-specific eval sets that reflect your actual product's use cases, not just leaderboard performance\nDesign human evaluation protocols where automated metrics fall short, and know which is which\nOwn regression detection: catching quality drops introduced by a new checkpoint, a prompt template change, or a serving optimization before they reach users- Contribute to safety and robustness evaluation — hallucination rate, adversarial/ red-team testing, and behavior under distribution shift — as a first-class part of the release process, not an afterthought\nWhat we're looking for\n4+ years of applied ML engineering experience, with at least **2 years working directly on large language models in a production context\nReal production deployment experience — you've shipped a model that served live traffic, and you can talk about the latency/cost/quality tradeoffs you made- Strong software engineering fundamentals\nFluency with the modern LLM tooling landscape (training frameworks, serving frameworks, eval tooling)\nComfort with ambiguity — you'll be asked to define what \"good\" means for a model behavior that doesn't have an established benchmark\nPay: $160,000.00 - $260,000.00 per year\nWork Location: Remote","datePosted":"2026-08-05T00:13:46.611Z","dateModified":"2026-08-05T00:13:46.611Z","hiringOrganization":{"@type":"Organization","name":"Placement Force","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote","addressRegion":"OR","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"35bd02fe02310954dcea3b8b"},"url":"https://jobsearcher.com/jobs/35bd02fe02310954dcea3b8b"}}